Summary of included studies

All studies

ExclusiveBF Non_exclusiveBF No_BF sum
Bangladesh 138 178 6 322
Canada 86 48 33 167
Haiti 37 11 0 48
South Africa 86 57 0 143
USA(CA_FL) 150 68 12 230
USA(CA_MA_MO) 38 66 116 220
USA(NC) 12 8 1 21
All studies 547 436 168 1151

All studies for stratified meta-analysis by birth mode

Vaginal C-section sum
Canada 130 37 167
Haiti 42 6 48
USA(CA_FL) 162 65 227
USA(CA_MA_MO) 150 78 228
All studies 484 186 670

Microbiome age

Analysis for microbiome age based on shared genera

Microbiome age was predicted based on the Random Forest model using L6 (genus) taxa relative abundance with list of taxa shared by all 7 included studies.

Plot of microbiome age by study all ages

With Generalized additive mixed model (GAMM) fit and 95%CI.

Sample age < 6 months only

Standardized microbiome age and GAMM fit comparison between bf group within each study

Meta-analysis for samples in <= 6 months old infants

Meta-analysis models based on adjusted estimate (adjusted for age of infant at sample collection) and standard error from linear mixed effect models.

Change of RM in non-exclusive breastfed (nebf) vs. exclusive breastfed (exbf)

                                           RD            95%-CI %W(fixed)
Thompson et al 2015 ( USA(NC) )        0.8754 [ 0.1366; 1.6142]       1.5
Wood et al 2017 ( South Africa )       0.1214 [-0.2963; 0.5390]       4.7
Pannaraj et al 2017 ( USA(CA_FL) )     0.2665 [ 0.0464; 0.4866]      17.1
Bender et al 2016 ( Haiti )           -0.1769 [-0.8203; 0.4664]       2.0
Subramanian et al 2014 ( Bangladesh )  0.0568 [-0.0605; 0.1741]      60.2
Sordillo et al 2017 ( USA(CA_MA_MO) )  0.7355 [ 0.3600; 1.1109]       5.9
Azad et al 2015 ( Canada )             0.6149 [ 0.3048; 0.9250]       8.6
                                      %W(random)
Thompson et al 2015 ( USA(NC) )              7.3
Wood et al 2017 ( South Africa )            13.4
Pannaraj et al 2017 ( USA(CA_FL) )          18.7
Bender et al 2016 ( Haiti )                  8.7
Subramanian et al 2014 ( Bangladesh )       21.0
Sordillo et al 2017 ( USA(CA_MA_MO) )       14.5
Azad et al 2015 ( Canada )                  16.3

Number of studies combined: k = 7

                         RD           95%-CI    z  p-value
Fixed effect model   0.1913 [0.1003; 0.2823] 4.12 < 0.0001
Random effects model 0.3336 [0.0894; 0.5778] 2.68   0.0074

Quantifying heterogeneity:
tau^2 = 0.0702; H = 2.06 [1.42; 2.98]; I^2 = 76.4% [50.4%; 88.8%]

Test of heterogeneity:
     Q d.f. p-value
 25.40    6  0.0003

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Thompson et al 2015 ( USA(NC) ) 0.0202100021212404
Wood et al 2017 ( South Africa ) 0.569010986228987
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0176451947099079
Bender et al 2016 ( Haiti ) 0.589859305402284
Subramanian et al 2014 ( Bangladesh ) 0.342537683608756
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.000123424504683378
Azad et al 2015 ( Canada ) 0.000101690866421991

Sensitivity analysis

No Haiti data

                                          RD            95%-CI %W(fixed)
Thompson et al 2015 ( USA(NC) )       0.8754 [ 0.1366; 1.6142]       1.5
Wood et al 2017 ( South Africa )      0.1214 [-0.2963; 0.5390]       4.8
Pannaraj et al 2017 ( USA(CA_FL) )    0.2665 [ 0.0464; 0.4866]      17.4
Subramanian et al 2014 ( Bangladesh ) 0.0568 [-0.0605; 0.1741]      61.4
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7355 [ 0.3600; 1.1109]       6.0
Azad et al 2015 ( Canada )            0.6149 [ 0.3048; 0.9250]       8.8
                                      %W(random)
Thompson et al 2015 ( USA(NC) )              8.1
Wood et al 2017 ( South Africa )            14.8
Pannaraj et al 2017 ( USA(CA_FL) )          20.4
Subramanian et al 2014 ( Bangladesh )       22.9
Sordillo et al 2017 ( USA(CA_MA_MO) )       15.9
Azad et al 2015 ( Canada )                  17.8

Number of studies combined: k = 6

                         RD           95%-CI    z  p-value
Fixed effect model   0.1988 [0.1069; 0.2908] 4.24 < 0.0001
Random effects model 0.3835 [0.1247; 0.6422] 2.90   0.0037

Quantifying heterogeneity:
tau^2 = 0.0726; H = 2.20 [1.49; 3.25]; I^2 = 79.3% [54.7%; 90.5%]

Test of heterogeneity:
     Q d.f. p-value
 24.11    5  0.0002

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Thompson et al 2015 ( USA(NC) ) 0.0202100021212404
Wood et al 2017 ( South Africa ) 0.569010986228987
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0176451947099079
Subramanian et al 2014 ( Bangladesh ) 0.342537683608756
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.000123424504683378
Azad et al 2015 ( Canada ) 0.000101690866421991

No USA(NC) data

                                           RD            95%-CI %W(fixed)
Wood et al 2017 ( South Africa )       0.1214 [-0.2963; 0.5390]       4.8
Pannaraj et al 2017 ( USA(CA_FL) )     0.2665 [ 0.0464; 0.4866]      17.4
Bender et al 2016 ( Haiti )           -0.1769 [-0.8203; 0.4664]       2.0
Subramanian et al 2014 ( Bangladesh )  0.0568 [-0.0605; 0.1741]      61.1
Sordillo et al 2017 ( USA(CA_MA_MO) )  0.7355 [ 0.3600; 1.1109]       6.0
Azad et al 2015 ( Canada )             0.6149 [ 0.3048; 0.9250]       8.7
                                      %W(random)
Wood et al 2017 ( South Africa )            14.3
Pannaraj et al 2017 ( USA(CA_FL) )          20.4
Bender et al 2016 ( Haiti )                  9.1
Subramanian et al 2014 ( Bangladesh )       23.1
Sordillo et al 2017 ( USA(CA_MA_MO) )       15.5
Azad et al 2015 ( Canada )                  17.6

Number of studies combined: k = 6

                         RD           95%-CI    z p-value
Fixed effect model   0.1808 [0.0891; 0.2725] 3.87  0.0001
Random effects model 0.2909 [0.0458; 0.5360] 2.33  0.0200

Quantifying heterogeneity:
tau^2 = 0.0640; H = 2.10 [1.41; 3.13]; I^2 = 77.3% [49.6%; 89.8%]

Test of heterogeneity:
     Q d.f. p-value
 22.05    5  0.0005

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Wood et al 2017 ( South Africa ) 0.569010986228987
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0176451947099079
Bender et al 2016 ( Haiti ) 0.589859305402284
Subramanian et al 2014 ( Bangladesh ) 0.342537683608756
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.000123424504683378
Azad et al 2015 ( Canada ) 0.000101690866421991

No USA(CA_MA_MO) data

                                           RD            95%-CI %W(fixed)
Thompson et al 2015 ( USA(NC) )        0.8754 [ 0.1366; 1.6142]       1.6
Wood et al 2017 ( South Africa )       0.1214 [-0.2963; 0.5390]       5.0
Pannaraj et al 2017 ( USA(CA_FL) )     0.2665 [ 0.0464; 0.4866]      18.2
Bender et al 2016 ( Haiti )           -0.1769 [-0.8203; 0.4664]       2.1
Subramanian et al 2014 ( Bangladesh )  0.0568 [-0.0605; 0.1741]      63.9
Azad et al 2015 ( Canada )             0.6149 [ 0.3048; 0.9250]       9.1
                                      %W(random)
Thompson et al 2015 ( USA(NC) )              7.4
Wood et al 2017 ( South Africa )            15.0
Pannaraj et al 2017 ( USA(CA_FL) )          22.8
Bender et al 2016 ( Haiti )                  9.0
Subramanian et al 2014 ( Bangladesh )       26.7
Azad et al 2015 ( Canada )                  19.0

Number of studies combined: k = 6

                         RD           95%-CI    z p-value
Fixed effect model   0.1574 [0.0636; 0.2512] 3.29  0.0010
Random effects model 0.2602 [0.0267; 0.4936] 2.18  0.0290

Quantifying heterogeneity:
tau^2 = 0.0495; H = 1.83 [1.20; 2.80]; I^2 = 70.3% [30.5%; 87.3%]

Test of heterogeneity:
     Q d.f. p-value
 16.83    5  0.0048

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Thompson et al 2015 ( USA(NC) ) 0.0202100021212404
Wood et al 2017 ( South Africa ) 0.569010986228987
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0176451947099079
Bender et al 2016 ( Haiti ) 0.589859305402284
Subramanian et al 2014 ( Bangladesh ) 0.342537683608756
Azad et al 2015 ( Canada ) 0.000101690866421991

Stratify by birth mode (not standardized)

Vaginal

                                         RD            95%-CI %W(fixed)
Azad et al 2015 (Canada)             2.0223 [ 1.0746; 2.9701]      23.9
Bender et al 2016 (Haiti)           -0.5119 [-2.7851; 1.7614]       4.1
Pannaraj et al 2017 (USA(CA_FL))     0.5934 [-0.0958; 1.2826]      45.1
Sordillo et al 2017 (USA(CA_MA_MO))  1.5749 [ 0.6821; 2.4677]      26.9
                                    %W(random)
Azad et al 2015 (Canada)                  27.7
Bender et al 2016 (Haiti)                 10.8
Pannaraj et al 2017 (USA(CA_FL))          32.8
Sordillo et al 2017 (USA(CA_MA_MO))       28.8

Number of studies combined: k = 4

                         RD           95%-CI    z  p-value
Fixed effect model   1.1523 [0.6894; 1.6152] 4.88 < 0.0001
Random effects model 1.1519 [0.2829; 2.0209] 2.60   0.0094

Quantifying heterogeneity:
tau^2 = 0.4763; H = 1.70 [1.00; 2.92]; I^2 = 65.4% [0.0%; 88.2%]

Test of heterogeneity:
    Q d.f. p-value
 8.68    3  0.0338

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Azad et al 2015 (Canada) 2.88635033937751e-05
Bender et al 2016 (Haiti) 0.658981004403214
Pannaraj et al 2017 (USA(CA_FL)) 0.0914811823311381
Sordillo et al 2017 (USA(CA_MA_MO)) 0.000545678208685348

C-section

                                         RD            95%-CI %W(fixed)
Azad et al 2015 (Canada)             0.6820 [-1.3391; 2.7031]      14.4
Bender et al 2016 (Haiti)           -0.3508 [-4.3974; 3.6958]       3.6
Pannaraj et al 2017 (USA(CA_FL))     0.7880 [-0.2996; 1.8755]      49.7
Sordillo et al 2017 (USA(CA_MA_MO))  1.1977 [-0.1522; 2.5476]      32.3
                                    %W(random)
Azad et al 2015 (Canada)                  14.4
Bender et al 2016 (Haiti)                  3.6
Pannaraj et al 2017 (USA(CA_FL))          49.7
Sordillo et al 2017 (USA(CA_MA_MO))       32.3

Number of studies combined: k = 4

                         RD           95%-CI    z p-value
Fixed effect model   0.8641 [0.0971; 1.6310] 2.21  0.0272
Random effects model 0.8641 [0.0971; 1.6310] 2.21  0.0272

Quantifying heterogeneity:
tau^2 = 0; H = 1.00 [1.00; 1.17]; I^2 = 0.0% [0.0%; 27.2%]

Test of heterogeneity:
    Q d.f. p-value
 0.63    3  0.8893

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Azad et al 2015 (Canada) 0.508396867999409
Bender et al 2016 (Haiti) 0.865079808120753
Pannaraj et al 2017 (USA(CA_FL)) 0.155598040795797
Sordillo et al 2017 (USA(CA_MA_MO)) 0.0820319111581724

Trend of microbiome age in exclusive breastfed (exbf), non-exclusive breastfed (nebf) and no bf

                                        RD            95%-CI %W(fixed)
Subramanian et al 2014 (Bangladesh) 0.0308 [-0.0757; 0.1372]      44.1
Azad et al 2015 (Canada)            0.5818 [ 0.4102; 0.7534]      17.0
Pannaraj et al 2017 (USA(CA_FL))    0.3161 [ 0.1504; 0.4819]      18.2
Sordillo et al 2017 (USA(CA_MA_MO)) 0.5214 [ 0.3626; 0.6802]      19.8
Thompson et al 2015 (USA(NC))       0.2911 [-0.4807; 1.0628]       0.8
                                    %W(random)
Subramanian et al 2014 (Bangladesh)       24.0
Azad et al 2015 (Canada)                  22.6
Pannaraj et al 2017 (USA(CA_FL))          22.7
Sordillo et al 2017 (USA(CA_MA_MO))       22.9
Thompson et al 2015 (USA(NC))              7.8

Number of studies combined: k = 5

                         RD           95%-CI    z  p-value
Fixed effect model   0.2758 [0.2051; 0.3465] 7.64 < 0.0001
Random effects model 0.3526 [0.0926; 0.6125] 2.66   0.0079

Quantifying heterogeneity:
tau^2 = 0.0703; H = 3.24 [2.28; 4.61]; I^2 = 90.5% [80.7%; 95.3%]

Test of heterogeneity:
     Q d.f.  p-value
 41.99    4 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 (Bangladesh) 0.570837554151296
Azad et al 2015 (Canada) 2.9999698377109e-11
Pannaraj et al 2017 (USA(CA_FL)) 0.000185565976276297
Sordillo et al 2017 (USA(CA_MA_MO)) 1.24192582145007e-10
Thompson et al 2015 (USA(NC)) 0.459754398037424

Exploratory analysis for effect of duration of exbf, formula and solid intro

With GAMM fit and 95%CI.

Subramanian (Bangladesh) data

Number of infants by duration of bf in the test set


<=2 months  >2 months 
        26         13 

Number of samples by duration of bf in the test set


<=2 months  >2 months 
       483        252 

Number of samples by duration of bf in the test set (>6 months only)


<=2 months  >2 months 
       305        176 

Duration (month) of exbf

Test for age > 6 months and <15months GAM part

Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.516456 0.4287802 26.858644 0.0000000
month.exbf2>2 months -1.634246 0.7401967 -2.207854 0.0280084
edf Ref.df F p-value
s(age.sample):month.exbf2<=2 months 1.000001 1.000001 120.79393 0
s(age.sample):month.exbf2>2 months 1.000000 1.000000 60.88972 0

LME part

Value Std.Error DF t-value p-value
X(Intercept) 11.516456 0.4302022 264 26.769865 0.0000000
Xmonth.exbf2>2 months -1.634246 0.7426508 37 -2.200557 0.0340874
Xs(age.sample):month.exbf2<=2 monthsFx1 2.352653 0.2147700 264 10.954291 0.0000000
Xs(age.sample):month.exbf2>2 monthsFx1 2.231823 0.2869630 264 7.777391 0.0000000

Performance of the Random Forest model to estimate microbiome age

Evaluated on the training and test set of Subramanian (Bangladesh) data.

List of shared taxa and their relative importance

genera importance
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia 3264.3029384
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ 1905.2788811
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__prevotellaceae.g__prevotella 935.6069474
k__bacteria.p__firmicutes.c__clostridia.oclostridiales.f.g__ 903.0123160
k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae.g__staphylococcus 693.0597449
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__dialister 465.3432501
k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__lactobacillaceae.g__lactobacillus 430.9372453
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae.g__haemophilus 412.8020722
k__bacteria.p__actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium 399.6333434
k__bacteria.p__actinobacteria.c__actinobacteria.o__actinomycetales.f__actinomycetaceae.g__actinomyces 326.5063233
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__dorea 232.9121374
k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__enterococcaceae.g__enterococcus 211.0289151
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus 184.2365345
k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__streptococcus 180.8771632
k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella 157.8024889
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__veillonella 157.1312508
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__clostridium 132.3348777
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__ruminococcaceae.g__oscillospira 127.3175910
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides 125.6500816
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pseudomonadales.f__pseudomonadaceae.g__pseudomonas 117.2762839
k__bacteria.p__actinobacteria.c__actinobacteria.o__actinomycetales.f__micrococcaceae.g__rothia 105.7002954
k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__ 100.0155111
k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__atopobium 66.9705451
k__bacteria.p__proteobacteria.c__betaproteobacteria.o__neisseriales.f__neisseriaceae.g__neisseria 59.3097361
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__porphyromonadaceae.g__parabacteroides 54.3713192
k__bacteria.p__proteobacteria.c__betaproteobacteria.o__burkholderiales.f__alcaligenaceae.g__sutterella 53.3836964
k__bacteria.p__fusobacteria.c__fusobacteriia.o__fusobacteriales.f__fusobacteriaceae.g__fusobacterium 52.3247474
k__bacteria.p__firmicutes.c__bacilli.o__gemellales.f__gemellaceae.g__ 51.8088217
k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__lactococcus 44.6428095
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__rikenellaceae.g__ 32.7066309
k__bacteria.p__cyanobacteria.c__chloroplast.ostreptophyta.f.g__ 10.7313468
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__acidaminococcus 7.1897234
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__roseburia 6.3092483
k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__carnobacteriaceae.g__granulicatella 4.2879708
k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__paenibacillaceae.g__paenibacillus 0.1393402
k__bacteria.p__proteobacteria.c__alphaproteobacteria.o__sphingomonadales.f__sphingomonadaceae.g__sphingomonas 0.1054538

Alpha diversity indexes

Plots of alpha diversity by study by age

With GAMM fit and 95%CI.

Samples <= 6 months only

GAMM fit comparison between bf group within each study

Samples <=6 months only

Exploration by duration of exclusive bf

Subramanian (Bangladesh) data only.

Exclusive bf duration

Shannon index.

Meta-analysis

For samples <=6 months old only.

Change in non-exbf vs. exbf

Chao1

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0677 [-0.0136; 0.1491]      79.0
Azad et al 2015 ( Canada )            0.3752 [ 0.0596; 0.6908]       5.3
Bender et al 2016 ( Haiti )           0.4922 [-0.3266; 1.3110]       0.8
Wood et al 2017 ( South Africa )      0.5113 [ 0.0923; 0.9303]       3.0
Pannaraj et al 2017 ( USA(CA_FL) )    0.1201 [-0.1362; 0.3764]       8.0
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.6457 [ 0.2430; 1.0485]       3.2
Thompson et al 2015 ( USA(NC) )       0.4111 [-0.4054; 1.2277]       0.8
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       28.0
Azad et al 2015 ( Canada )                  16.8
Bender et al 2016 ( Haiti )                  4.9
Wood et al 2017 ( South Africa )            12.6
Pannaraj et al 2017 ( USA(CA_FL) )          19.6
Sordillo et al 2017 ( USA(CA_MA_MO) )       13.2
Thompson et al 2015 ( USA(NC) )              4.9

Number of studies combined: k = 7

                         RD           95%-CI    z p-value
Fixed effect model   0.1259 [0.0536; 0.1982] 3.41  0.0006
Random effects model 0.2995 [0.1020; 0.4969] 2.97  0.0030

Quantifying heterogeneity:
tau^2 = 0.0346; H = 1.59 [1.05; 2.41]; I^2 = 60.7% [9.9%; 82.8%]

Test of heterogeneity:
     Q d.f. p-value
 15.25    6  0.0184

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.102847325927163
Azad et al 2015 ( Canada ) 0.01980051110656
Bender et al 2016 ( Haiti ) 0.238756122389415
Wood et al 2017 ( South Africa ) 0.0167677830041401
Pannaraj et al 2017 ( USA(CA_FL) ) 0.358399385859461
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.00167635766494574
Thompson et al 2015 ( USA(NC) ) 0.32371285335117

Observed_species

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0673 [-0.0127; 0.1473]      77.1
Azad et al 2015 ( Canada )            0.3721 [ 0.0590; 0.6852]       5.0
Bender et al 2016 ( Haiti )           0.4601 [-0.3413; 1.2616]       0.8
Wood et al 2017 ( South Africa )      0.4996 [ 0.0848; 0.9143]       2.9
Pannaraj et al 2017 ( USA(CA_FL) )    0.1447 [-0.0763; 0.3657]      10.1
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.6461 [ 0.2454; 1.0467]       3.1
Thompson et al 2015 ( USA(NC) )       0.3555 [-0.3448; 1.0557]       1.0
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       28.2
Azad et al 2015 ( Canada )                  16.1
Bender et al 2016 ( Haiti )                  4.5
Wood et al 2017 ( South Africa )            12.0
Pannaraj et al 2017 ( USA(CA_FL) )          21.0
Sordillo et al 2017 ( USA(CA_MA_MO) )       12.5
Thompson et al 2015 ( USA(NC) )              5.7

Number of studies combined: k = 7

                         RD           95%-CI    z p-value
Fixed effect model   0.1266 [0.0564; 0.1969] 3.53  0.0004
Random effects model 0.2909 [0.1053; 0.4765] 3.07  0.0021

Quantifying heterogeneity:
tau^2 = 0.0301; H = 1.59 [1.05; 2.41]; I^2 = 60.4% [9.1%; 82.7%]

Test of heterogeneity:
     Q d.f. p-value
 15.14    6  0.0192

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.0990100956520888
Azad et al 2015 ( Canada ) 0.019839103185688
Bender et al 2016 ( Haiti ) 0.260501865374435
Wood et al 2017 ( South Africa ) 0.0182288177251842
Pannaraj et al 2017 ( USA(CA_FL) ) 0.19952096913775
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.00157427402976728
Thompson et al 2015 ( USA(NC) ) 0.319751771051308

Pd_whole_tree

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0953 [ 0.0196; 0.1710]      80.5
Azad et al 2015 ( Canada )            0.2877 [-0.0327; 0.6082]       4.5
Bender et al 2016 ( Haiti )           0.3082 [-0.3766; 0.9931]       1.0
Wood et al 2017 ( South Africa )      0.4724 [ 0.0489; 0.8958]       2.6
Pannaraj et al 2017 ( USA(CA_FL) )    0.2168 [ 0.0121; 0.4215]      11.0
Thompson et al 2015 ( USA(NC) )       0.7261 [-0.2385; 1.6906]       0.5
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       52.8
Azad et al 2015 ( Canada )                  12.0
Bender et al 2016 ( Haiti )                  3.1
Wood et al 2017 ( South Africa )             7.4
Pannaraj et al 2017 ( USA(CA_FL) )          23.2
Thompson et al 2015 ( USA(NC) )              1.6

Number of studies combined: k = 6

                         RD           95%-CI    z p-value
Fixed effect model   0.1322 [0.0643; 0.2001] 3.82  0.0001
Random effects model 0.1910 [0.0685; 0.3134] 3.06  0.0022

Quantifying heterogeneity:
tau^2 = 0.0059; H = 1.15 [1.00; 1.78]; I^2 = 25.0% [0.0%; 68.4%]

Test of heterogeneity:
    Q d.f. p-value
 6.66    5  0.2469

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.0136266394489582
Azad et al 2015 ( Canada ) 0.0784131649118116
Bender et al 2016 ( Haiti ) 0.377692577417145
Wood et al 2017 ( South Africa ) 0.0288048675369895
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0379096968161372
Thompson et al 2015 ( USA(NC) ) 0.140133230873501

Shannon

                                           RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh )  0.2592 [ 0.1185; 0.3999]      57.3
Azad et al 2015 ( Canada )             0.3262 [ 0.0159; 0.6365]      11.8
Bender et al 2016 ( Haiti )           -0.1146 [-0.7954; 0.5662]       2.4
Wood et al 2017 ( South Africa )       0.3071 [-0.1311; 0.7452]       5.9
Pannaraj et al 2017 ( USA(CA_FL) )     0.3732 [ 0.0808; 0.6657]      13.3
Sordillo et al 2017 ( USA(CA_MA_MO) )  0.7684 [ 0.3821; 1.1546]       7.6
Thompson et al 2015 ( USA(NC) )        0.3001 [-0.5308; 1.1310]       1.6
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       40.5
Azad et al 2015 ( Canada )                  15.7
Bender et al 2016 ( Haiti )                  4.0
Wood et al 2017 ( South Africa )             8.9
Pannaraj et al 2017 ( USA(CA_FL) )          17.2
Sordillo et al 2017 ( USA(CA_MA_MO) )       11.0
Thompson et al 2015 ( USA(NC) )              2.7

Number of studies combined: k = 7

                         RD           95%-CI    z  p-value
Fixed effect model   0.3153 [0.2088; 0.4219] 5.80 < 0.0001
Random effects model 0.3359 [0.1959; 0.4759] 4.70 < 0.0001

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.12 [1.00; 1.67]; I^2 = 20.9% [0.0%; 64.3%]

Test of heterogeneity:
    Q d.f. p-value
 7.59    6  0.2700

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.000305463231694058
Azad et al 2015 ( Canada ) 0.0393559233405728
Bender et al 2016 ( Haiti ) 0.74142212926805
Wood et al 2017 ( South Africa ) 0.169568310346119
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0123688713005407
Sordillo et al 2017 ( USA(CA_MA_MO) ) 9.65594918082499e-05
Thompson et al 2015 ( USA(NC) ) 0.479014124949741

Sensitivity analysis Change in non-exbf vs. exbf

Show the results of Shannon indexes only.

No Haiti data

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.2592 [ 0.1185; 0.3999]      58.8
Azad et al 2015 ( Canada )            0.3262 [ 0.0159; 0.6365]      12.1
Wood et al 2017 ( South Africa )      0.3071 [-0.1311; 0.7452]       6.1
Pannaraj et al 2017 ( USA(CA_FL) )    0.3732 [ 0.0808; 0.6657]      13.6
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.7684 [ 0.3821; 1.1546]       7.8
Thompson et al 2015 ( USA(NC) )       0.3001 [-0.5308; 1.1310]       1.7
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       45.8
Azad et al 2015 ( Canada )                  15.5
Wood et al 2017 ( South Africa )             8.5
Pannaraj et al 2017 ( USA(CA_FL) )          17.1
Sordillo et al 2017 ( USA(CA_MA_MO) )       10.6
Thompson et al 2015 ( USA(NC) )              2.5

Number of studies combined: k = 6

                         RD           95%-CI    z  p-value
Fixed effect model   0.3261 [0.2183; 0.4340] 5.93 < 0.0001
Random effects model 0.3483 [0.2145; 0.4821] 5.10 < 0.0001

Quantifying heterogeneity:
tau^2 = 0.0050; H = 1.10 [1.00; 1.61]; I^2 = 16.9% [0.0%; 61.6%]

Test of heterogeneity:
    Q d.f. p-value
 6.02    5  0.3047

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.000305463231694058
Azad et al 2015 ( Canada ) 0.0393559233405728
Wood et al 2017 ( South Africa ) 0.169568310346119
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0123688713005407
Sordillo et al 2017 ( USA(CA_MA_MO) ) 9.65594918082499e-05
Thompson et al 2015 ( USA(NC) ) 0.479014124949741

No UNC data

                                           RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh )  0.2592 [ 0.1185; 0.3999]      58.3
Azad et al 2015 ( Canada )             0.3262 [ 0.0159; 0.6365]      12.0
Bender et al 2016 ( Haiti )           -0.1146 [-0.7954; 0.5662]       2.5
Wood et al 2017 ( South Africa )       0.3071 [-0.1311; 0.7452]       6.0
Pannaraj et al 2017 ( USA(CA_FL) )     0.3732 [ 0.0808; 0.6657]      13.5
Sordillo et al 2017 ( USA(CA_MA_MO) )  0.7684 [ 0.3821; 1.1546]       7.7
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       36.5
Azad et al 2015 ( Canada )                  17.2
Bender et al 2016 ( Haiti )                  4.9
Wood et al 2017 ( South Africa )            10.3
Pannaraj et al 2017 ( USA(CA_FL) )          18.6
Sordillo et al 2017 ( USA(CA_MA_MO) )       12.6

Number of studies combined: k = 6

                         RD           95%-CI    z  p-value
Fixed effect model   0.3156 [0.2082; 0.4230] 5.76 < 0.0001
Random effects model 0.3427 [0.1851; 0.5004] 4.26 < 0.0001

Quantifying heterogeneity:
tau^2 = 0.0126; H = 1.23 [1.00; 1.94]; I^2 = 34.1% [0.0%; 73.5%]

Test of heterogeneity:
    Q d.f. p-value
 7.58    5  0.1807

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.000305463231694058
Azad et al 2015 ( Canada ) 0.0393559233405728
Bender et al 2016 ( Haiti ) 0.74142212926805
Wood et al 2017 ( South Africa ) 0.169568310346119
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0123688713005407
Sordillo et al 2017 ( USA(CA_MA_MO) ) 9.65594918082499e-05

No USA(CA_MA_MO) data

                                           RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh )  0.2592 [ 0.1185; 0.3999]      62.0
Azad et al 2015 ( Canada )             0.3262 [ 0.0159; 0.6365]      12.8
Bender et al 2016 ( Haiti )           -0.1146 [-0.7954; 0.5662]       2.7
Wood et al 2017 ( South Africa )       0.3071 [-0.1311; 0.7452]       6.4
Pannaraj et al 2017 ( USA(CA_FL) )     0.3732 [ 0.0808; 0.6657]      14.4
Thompson et al 2015 ( USA(NC) )        0.3001 [-0.5308; 1.1310]       1.8
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       62.0
Azad et al 2015 ( Canada )                  12.8
Bender et al 2016 ( Haiti )                  2.7
Wood et al 2017 ( South Africa )             6.4
Pannaraj et al 2017 ( USA(CA_FL) )          14.4
Thompson et al 2015 ( USA(NC) )              1.8

Number of studies combined: k = 6

                         RD           95%-CI    z  p-value
Fixed effect model   0.2780 [0.1672; 0.3889] 4.92 < 0.0001
Random effects model 0.2780 [0.1672; 0.3889] 4.92 < 0.0001

Quantifying heterogeneity:
tau^2 = 0; H = 1.00 [1.00; 1.21]; I^2 = 0.0% [0.0%; 32.0%]

Test of heterogeneity:
    Q d.f. p-value
 1.87    5  0.8674

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.000305463231694058
Azad et al 2015 ( Canada ) 0.0393559233405728
Bender et al 2016 ( Haiti ) 0.74142212926805
Wood et al 2017 ( South Africa ) 0.169568310346119
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0123688713005407
Thompson et al 2015 ( USA(NC) ) 0.479014124949741

Stratify by birth mode for Change in non-exbf vs. exbf (not standardized)

Show results of Shannon index only.

Vaginal

                                         RD            95%-CI %W(fixed)
 Azad et al 2015 (Canada)            0.1424 [-0.0984; 0.3832]      40.8
Bender et al 2016 (Haiti)           -0.2018 [-0.9752; 0.5715]       4.0
Pannaraj et al 2017 (USA(CA_FL))     0.3734 [ 0.1294; 0.6174]      39.7
Sordillo et al 2017 (USA(CA_MA_MO))  0.4321 [ 0.0420; 0.8223]      15.5
                                    %W(random)
 Azad et al 2015 (Canada)                 38.3
Bender et al 2016 (Haiti)                  5.4
Pannaraj et al 2017 (USA(CA_FL))          37.6
Sordillo et al 2017 (USA(CA_MA_MO))       18.6

Number of studies combined: k = 4

                         RD           95%-CI    z p-value
Fixed effect model   0.2656 [0.1118; 0.4194] 3.38  0.0007
Random effects model 0.2647 [0.0798; 0.4495] 2.81  0.0050

Quantifying heterogeneity:
tau^2 = 0.0081; H = 1.13 [1.00; 2.90]; I^2 = 22.3% [0.0%; 88.1%]

Test of heterogeneity:
    Q d.f. p-value
 3.86    3  0.2771

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Azad et al 2015 (Canada) 0.246532761397953
Bender et al 2016 (Haiti) 0.608996225108868
Pannaraj et al 2017 (USA(CA_FL)) 0.00270151112577258
Sordillo et al 2017 (USA(CA_MA_MO)) 0.0299341350698865

C-section

                                         RD            95%-CI %W(fixed)
 Azad et al 2015 (Canada)            0.4873 [ 0.0230; 0.9515]      40.3
Pannaraj et al 2017 (USA(CA_FL))    -0.0737 [-0.5869; 0.4395]      33.0
Sordillo et al 2017 (USA(CA_MA_MO))  1.0833 [ 0.5127; 1.6540]      26.7
                                    %W(random)
 Azad et al 2015 (Canada)                 34.9
Pannaraj et al 2017 (USA(CA_FL))          33.4
Sordillo et al 2017 (USA(CA_MA_MO))       31.7

Number of studies combined: k = 3

                         RD            95%-CI    z p-value
Fixed effect model   0.4612 [ 0.1665; 0.7560] 3.07  0.0022
Random effects model 0.4888 [-0.1327; 1.1103] 1.54  0.1232

Quantifying heterogeneity:
tau^2 = 0.2323; H = 2.09 [1.16; 3.77]; I^2 = 77.1% [25.6%; 93.0%]

Test of heterogeneity:
    Q d.f. p-value
 8.75    2  0.0126

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Azad et al 2015 (Canada) 0.0396622679428872
Pannaraj et al 2017 (USA(CA_FL)) 0.778417002121859
Sordillo et al 2017 (USA(CA_MA_MO)) 0.000198560001433614

Put meta-analysis results (random models) of all indexes together

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
shannon 0.3359260 0.0714283 0.1959290 0.4759229 4.702980 0.0000026 0.0000103
observed_species 0.2908894 0.0946918 0.1052969 0.4764819 3.071960 0.0021266 0.0029552
pd_whole_tree 0.1909514 0.0624970 0.0684595 0.3134432 3.055369 0.0022478 0.0029552
chao1 0.2994547 0.1007464 0.1019953 0.4969141 2.972360 0.0029552 0.0029552

Trend effect in exbf, non-exbf, no bf

Chao1

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0550 [-0.0189; 0.1289]      65.2
Azad et al 2015 ( Canada )            0.5725 [ 0.3986; 0.7464]      11.8
Bender et al 2016 ( Haiti )               NA                         0.0
Wood et al 2017 ( South Africa )          NA                         0.0
Pannaraj et al 2017 ( USA(CA_FL) )    0.1302 [-0.0599; 0.3202]       9.9
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.4245 [ 0.2553; 0.5937]      12.4
Thompson et al 2015 ( USA(NC) )       0.4233 [-0.2687; 1.1153]       0.7
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       24.7
Azad et al 2015 ( Canada )                  22.4
Bender et al 2016 ( Haiti )                  0.0
Wood et al 2017 ( South Africa )             0.0
Pannaraj et al 2017 ( USA(CA_FL) )          21.9
Sordillo et al 2017 ( USA(CA_MA_MO) )       22.6
Thompson et al 2015 ( USA(NC) )              8.3

Number of studies combined: k = 5

                         RD           95%-CI    z  p-value
Fixed effect model   0.1720 [0.1123; 0.2316] 5.65 < 0.0001
Random effects model 0.3016 [0.0580; 0.5451] 2.43   0.0152

Quantifying heterogeneity:
tau^2 = 0.0610; H = 3.13 [2.19; 4.49]; I^2 = 89.8% [79.1%; 95.0%]

Test of heterogeneity:
     Q d.f.  p-value
 39.26    4 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.144460866036347
Azad et al 2015 ( Canada ) 1.0991009354299e-10
Bender et al 2016 ( Haiti ) NA
Wood et al 2017 ( South Africa ) NA
Pannaraj et al 2017 ( USA(CA_FL) ) 0.179455833167877
Sordillo et al 2017 ( USA(CA_MA_MO) ) 8.77670396356771e-07
Thompson et al 2015 ( USA(NC) ) 0.230600549600578

Observed_species

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0603 [-0.0124; 0.1329]      63.5
Azad et al 2015 ( Canada )            0.5895 [ 0.4168; 0.7623]      11.2
Bender et al 2016 ( Haiti )               NA                         0.0
Wood et al 2017 ( South Africa )          NA                         0.0
Pannaraj et al 2017 ( USA(CA_FL) )    0.1163 [-0.0479; 0.2806]      12.4
Sordillo et al 2017 ( USA(CA_MA_MO) ) 0.4610 [ 0.2928; 0.6291]      11.9
Thompson et al 2015 ( USA(NC) )       0.3661 [-0.2275; 0.9598]       1.0
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       24.1
Azad et al 2015 ( Canada )                  21.9
Bender et al 2016 ( Haiti )                  0.0
Wood et al 2017 ( South Africa )             0.0
Pannaraj et al 2017 ( USA(CA_FL) )          22.1
Sordillo et al 2017 ( USA(CA_MA_MO) )       22.0
Thompson et al 2015 ( USA(NC) )             10.0

Number of studies combined: k = 5

                         RD           95%-CI    z  p-value
Fixed effect model   0.1772 [0.1192; 0.2351] 6.00 < 0.0001
Random effects model 0.3070 [0.0641; 0.5500] 2.48   0.0133

Quantifying heterogeneity:
tau^2 = 0.0625; H = 3.30 [2.33; 4.69]; I^2 = 90.8% [81.6%; 95.4%]

Test of heterogeneity:
     Q d.f.  p-value
 43.68    4 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.104033167825551
Azad et al 2015 ( Canada ) 2.26960893212611e-11
Bender et al 2016 ( Haiti ) NA
Wood et al 2017 ( South Africa ) NA
Pannaraj et al 2017 ( USA(CA_FL) ) 0.165000455562882
Sordillo et al 2017 ( USA(CA_MA_MO) ) 7.73447284594136e-08
Thompson et al 2015 ( USA(NC) ) 0.226684364502839

Pd_whole_tree

                                          RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh ) 0.0646 [-0.0047; 0.1338]      72.9
Azad et al 2015 ( Canada )            0.5311 [ 0.3540; 0.7082]      11.2
Bender et al 2016 ( Haiti )               NA                         0.0
Wood et al 2017 ( South Africa )          NA                         0.0
Pannaraj et al 2017 ( USA(CA_FL) )    0.2032 [ 0.0523; 0.3541]      15.4
Thompson et al 2015 ( USA(NC) )       0.8566 [ 0.0509; 1.6623]       0.5
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       32.9
Azad et al 2015 ( Canada )                  29.1
Bender et al 2016 ( Haiti )                  0.0
Wood et al 2017 ( South Africa )             0.0
Pannaraj et al 2017 ( USA(CA_FL) )          30.2
Thompson et al 2015 ( USA(NC) )              7.8

Number of studies combined: k = 4

                         RD           95%-CI    z  p-value
Fixed effect model   0.1422 [0.0830; 0.2013] 4.71 < 0.0001
Random effects model 0.3037 [0.0475; 0.5598] 2.32   0.0202

Quantifying heterogeneity:
tau^2 = 0.0506; H = 3.00 [1.97; 4.57]; I^2 = 88.9% [74.2%; 95.2%]

Test of heterogeneity:
     Q d.f.  p-value
 26.99    3 < 0.0001

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.0676383461236607
Azad et al 2015 ( Canada ) 4.17049539317814e-09
Bender et al 2016 ( Haiti ) NA
Wood et al 2017 ( South Africa ) NA
Pannaraj et al 2017 ( USA(CA_FL) ) 0.00831447939658577
Thompson et al 2015 ( USA(NC) ) 0.0371791605971582

Shannon

                                           RD            95%-CI %W(fixed)
Subramanian et al 2014 ( Bangladesh )  0.2131 [ 0.0843; 0.3420]      39.0
Azad et al 2015 ( Canada )             0.5872 [ 0.4153; 0.7590]      21.9
Pannaraj et al 2017 ( USA(CA_FL) )     0.2570 [ 0.0375; 0.4765]      13.5
Sordillo et al 2017 ( USA(CA_MA_MO) )  0.5660 [ 0.4038; 0.7282]      24.6
Thompson et al 2015 ( USA(NC) )       -0.0180 [-0.8408; 0.8049]       1.0
                                      %W(random)
Subramanian et al 2014 ( Bangladesh )       25.8
Azad et al 2015 ( Canada )                  23.8
Pannaraj et al 2017 ( USA(CA_FL) )          21.4
Sordillo et al 2017 ( USA(CA_MA_MO) )       24.2
Thompson et al 2015 ( USA(NC) )              4.8

Number of studies combined: k = 5

                         RD           95%-CI    z  p-value
Fixed effect model   0.3858 [0.3053; 0.4663] 9.39 < 0.0001
Random effects model 0.3857 [0.1872; 0.5843] 3.81   0.0001

Quantifying heterogeneity:
tau^2 = 0.0355; H = 2.19 [1.42; 3.37]; I^2 = 79.1% [50.4%; 91.2%]

Test of heterogeneity:
     Q d.f. p-value
 19.16    4  0.0007

Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
study pval
Subramanian et al 2014 ( Bangladesh ) 0.00119013497616748
Azad et al 2015 ( Canada ) 2.14964602978342e-11
Pannaraj et al 2017 ( USA(CA_FL) ) 0.0217486284946625
Sordillo et al 2017 ( USA(CA_MA_MO) ) 7.9318409863511e-12
Thompson et al 2015 ( USA(NC) ) 0.965887658685873

Put meta-analysis results (random models) of all indexes together

estimate.conbf se.conbf ll.conbf ul.conbf z.conbf p.conbf p.adjust.conbf
shannon 0.3857475 0.1012964 0.1872101 0.5842849 3.808105 0.0001400 0.0005601
observed_species 0.3070476 0.1239712 0.0640686 0.5500267 2.476766 0.0132579 0.0201585
pd_whole_tree 0.3036608 0.1306976 0.0474982 0.5598233 2.323384 0.0201585 0.0201585
chao1 0.3015700 0.1242513 0.0580420 0.5450980 2.427098 0.0152201 0.0201585

Meta-analysis of taxa relative abundance

Results of 7 studies.

For samples <= 6 months old only in all studies (note for USA(NC) study: GAMLSS BEZI with random subject effect could not run on very small sample size=> did not include subject random effect). Results of random meta-analysis models for taxa available in at least >50% of studies based on adjusted estimates and standard errors from GAMLSS models with zero-inflated beta family adjusted for infant age at sample collection.

Meta-analysis of Change in non-exbf vs. exbf adjusted for age

GAMLSS models with zero-inflated beta family

Phylum (l2)

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes 0.2477163 0.0684909 0.1134765 0.3819560 3.616776 0.0002983 0.0017898
k__bacteria.p__bacteroidetes 0.2079683 0.0756683 0.0596613 0.3562754 2.748422 0.0059883 0.0179648

Nice plot

Order (l4)

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales 0.3015251 0.0904382 0.1242695 0.4787808 3.334046 0.0008559 0.0105564
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales 0.2100511 0.0757620 0.0615603 0.3585419 2.772512 0.0055625 0.0369277
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales 0.2005008 0.0896354 0.0248186 0.3761830 2.236847 0.0252963 0.1169955

Nice plot

Family (l5)

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae 0.2311548 0.0856673 0.0632499 0.3990596 2.698284 0.0069698 0.0756721
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae 0.2107748 0.0807013 0.0526031 0.3689465 2.611789 0.0090070 0.0855663
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae 0.2005008 0.0896354 0.0248186 0.3761830 2.236847 0.0252963 0.1747746
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae 0.1652266 0.0841374 0.0003203 0.3301328 1.963771 0.0495566 0.3086619

Nice plot

Genus (l6)

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. 0.3926058 0.1237182 0.1501225 0.6350890 3.173387 0.0015067 0.0561251
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera 0.4000827 0.1416041 0.1225438 0.6776217 2.825361 0.0047227 0.1115318
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides 0.2107748 0.0807013 0.0526031 0.3689465 2.611789 0.0090070 0.1220036
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium 0.3454360 0.1505929 0.0502793 0.6405927 2.293839 0.0217997 0.2512768
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__veillonella 0.2136555 0.1033136 0.0111645 0.4161465 2.068028 0.0386374 0.3598107

Nice plot

Sensitivity meta-analysis of Change in non-exbf vs. exbf adjusted for age

No UNC data

l2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes 0.2469023 0.0691970 0.1112786 0.3825259 3.568106 0.0003596 0.0025170
k__bacteria.p__bacteroidetes 0.1932696 0.0763734 0.0435804 0.3429588 2.530586 0.0113872 0.0398552

l4

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales 0.2819748 0.0889258 0.1076835 0.4562662 3.170900 0.0015197 0.0233017
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales 0.1953538 0.0764693 0.0454767 0.3452308 2.554669 0.0106289 0.0873019
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales 0.1990864 0.0908867 0.0209516 0.3772211 2.190489 0.0284888 0.1638107

l5

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae 0.1947860 0.0815355 0.0349794 0.3545925 2.388972 0.0168956 0.2092121
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae 0.2232183 0.0940439 0.0388955 0.4075410 2.373553 0.0176179 0.2092121
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae 0.1990864 0.0908867 0.0209516 0.3772211 2.190489 0.0284888 0.2460398

l6

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. 0.3970460 0.1275892 0.1469758 0.6471162 3.111909 0.0018588 0.0887585
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera 0.4000827 0.1416041 0.1225438 0.6776217 2.825361 0.0047227 0.1804084
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ 0.2425407 0.0943469 0.0576242 0.4274571 2.570734 0.0101483 0.2416618
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides 0.1947860 0.0815355 0.0349794 0.3545925 2.388972 0.0168956 0.2804176
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium 0.3454360 0.1505929 0.0502793 0.6405927 2.293839 0.0217997 0.3202883

No Haiti data

l2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes 0.2585644 0.0699441 0.1214765 0.3956523 3.696729 0.0002184 0.0013104
k__bacteria.p__bacteroidetes 0.2137713 0.0769990 0.0628560 0.3646866 2.776286 0.0054984 0.0164951

l4

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales 0.3197985 0.0708228 0.1809883 0.4586087 4.515471 0.0000063 0.0001200
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales 0.2201357 0.0770481 0.0691242 0.3711471 2.857121 0.0042750 0.0324902
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales 0.1839766 0.0915095 0.0046213 0.3633320 2.010465 0.0443820 0.2108145

l5

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae 0.2061111 0.0822865 0.0448325 0.3673898 2.504798 0.0122521 0.1347734
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae 0.2060989 0.0969374 0.0161050 0.3960927 2.126103 0.0334947 0.2946724
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae 0.1839766 0.0915095 0.0046213 0.3633320 2.010465 0.0443820 0.2946724
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae 0.1733438 0.0868412 0.0031382 0.3435495 1.996101 0.0459230 0.2946724

l6

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera 0.4521725 0.1519683 0.1543202 0.7500249 2.975441 0.0029257 0.1062738
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. 0.3594531 0.1297330 0.1051810 0.6137251 2.770714 0.0055934 0.1062738
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides 0.2061111 0.0822865 0.0448325 0.3673898 2.504798 0.0122521 0.1862323
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium 0.3557799 0.1521514 0.0575687 0.6539911 2.338328 0.0193702 0.2676612
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus 0.5201042 0.2401244 0.0494691 0.9907393 2.165979 0.0303128 0.3839625
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ 0.5344090 0.2586130 0.0275369 1.0412811 2.066443 0.0387867 0.3877940

No USA(CA_MA_MO) data

l2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__bacteroidetes 0.2312551 0.0810268 0.0724455 0.3900646 2.854058 0.0043165 0.0250675
k__bacteria.p__firmicutes 0.2006141 0.0745996 0.0544015 0.3468267 2.689210 0.0071621 0.0250675

l4

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales 0.2337644 0.0811408 0.0747312 0.3927975 2.880970 0.0039645 0.0549097
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales 0.2968923 0.1099207 0.0814516 0.5123329 2.700967 0.0069138 0.0549097
k__bacteria.p__actinobacteria.c__actinobacteria.o__actinomycetales -0.1557879 0.0644216 -0.2820518 -0.0295239 -2.418256 0.0155951 0.1024820
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales 0.2117009 0.0993562 0.0169663 0.4064356 2.130727 0.0331117 0.1692374
k__bacteria.p__firmicutes.c__bacilli.o__bacillales -0.2108602 0.1040509 -0.4147961 -0.0069242 -2.026511 0.0427125 0.1964774

l5

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae 0.2996094 0.0741081 0.1543601 0.4448586 4.042869 0.0000528 0.0050161
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae 0.2375149 0.0870795 0.0668422 0.4081877 2.727563 0.0063804 0.0850503
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae 0.2117009 0.0993562 0.0169663 0.4064356 2.130727 0.0331117 0.2621341

l6

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__acidaminococcus 2.0640609 0.5761745 0.9347797 3.1933421 3.582354 0.0003405 0.0306461
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. 0.4120255 0.1362642 0.1449526 0.6790984 3.023725 0.0024968 0.1074319
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__megasphaera 0.4531996 0.1618768 0.1359269 0.7704723 2.799657 0.0051157 0.1074319
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides 0.2375149 0.0870795 0.0668422 0.4081877 2.727563 0.0063804 0.1074319
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__veillonella 0.2611018 0.1085724 0.0483038 0.4738997 2.404864 0.0161785 0.2080092
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__clostridium 0.3454360 0.1505929 0.0502793 0.6405927 2.293839 0.0217997 0.2615967
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus 0.5470330 0.2571987 0.0429329 1.0511331 2.126889 0.0334293 0.3166986

Stratified analysis by birth mode for change in non-exbf vs. exbf adjusted for age

Vaginal

l2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__proteobacteria -0.3069577 0.1017253 -0.5063356 -0.1075798 -3.017517 0.0025486 0.0178399

l4

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales -0.2990541 0.1074593 -0.5096705 -0.0884377 -2.782951 0.0053867 0.1158139
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales 0.2827724 0.1232153 0.0412747 0.5242700 2.294944 0.0217363 0.2336654

l5

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae -0.3327091 0.1174047 -0.5628181 -0.1026001 -2.833865 0.0045989 0.1157163
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae -0.2990541 0.1074593 -0.5096705 -0.0884377 -2.782951 0.0053867 0.1157163
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae 0.3016820 0.1105580 0.0849923 0.5183717 2.728721 0.0063580 0.1157163
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__eubacteriaceae 0.8045605 0.3139899 0.1891515 1.4199695 2.562377 0.0103958 0.1576704
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae 0.2827724 0.1232153 0.0412747 0.5242700 2.294944 0.0217363 0.2197783

l6

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__acidaminococcus 3.3685160 0.4300060 2.5257196 4.2113123 7.833648 0.0000000 0.0000000
k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae.g__staphylococcus -0.3330596 0.1174105 -0.5631799 -0.1029393 -2.836711 0.0045581 0.1176238
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__eubacteriaceae.g__pseudoramibacter_eubacterium 0.8762232 0.3157991 0.2572683 1.4951780 2.774622 0.0055266 0.1176238
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ 0.3180415 0.1163513 0.0899972 0.5460858 2.733460 0.0062673 0.1176238
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales.f__bacteroidaceae.g__bacteroides 0.3016820 0.1105580 0.0849923 0.5183717 2.728721 0.0063580 0.1176238
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. 0.5384916 0.2026827 0.1412408 0.9357425 2.656820 0.0078881 0.1326643
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae.g__ -0.2862286 0.1148106 -0.5112532 -0.0612040 -2.493051 0.0126651 0.1802335
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ 0.3027671 0.1238937 0.0599398 0.5455944 2.443764 0.0145349 0.1920686
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae.g__aggregatibacter 0.5722694 0.2477375 0.0867128 1.0578260 2.309983 0.0208891 0.2234010
k__bacteria.p__firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__lactococcus 0.5835624 0.2676966 0.0588868 1.1082381 2.179940 0.0292619 0.2849187
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia 0.3639237 0.1848815 0.0015626 0.7262849 1.968416 0.0490202 0.4534368

C-section

l2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__proteobacteria -0.719666 0.1708821 -1.054589 -0.3847433 -4.211478 2.54e-05 0.0001522

l4

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales -0.5794373 0.2324176 -1.034968 -0.1239071 -2.493087 0.0126638 0.1561867

l5

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae -0.5794373 0.2324176 -1.034968 -0.1239071 -2.493087 0.0126638 0.2596077

l6

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae.g__proteus -0.2578897 0.0008395 -0.2595351 -0.2562442 -307.189026 0.0000000 0.0000000
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__ruminococcaceae.g__anaerotruncus -2.9244379 0.7060795 -4.3083283 -1.5405475 -4.141797 0.0000345 0.0018723
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__veillonellaceae.g__phascolarctobacterium -1.8840686 0.8567398 -3.5632478 -0.2048894 -2.199114 0.0278698 0.6489685
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__enterobacteriales.f__enterobacteriaceae.g__ -0.5225859 0.2608359 -1.0338148 -0.0113570 -2.003505 0.0451232 0.9153914

Meta-analysis of Trend in exbf, non-exbf and no bf adjusted for age

Phylum (l2)

Significant (pooled p<0.05) only

estimate.conbf se.conbf ll.conbf ul.conbf z.conbf p.conbf p.adjust.conbf
k__bacteria.p__firmicutes 0.2891066 0.0810164 0.1303173 0.4478959 3.568494 0.0003590 0.0021542
k__bacteria.p__verrucomicrobia 0.1902821 0.0798894 0.0337017 0.3468625 2.381818 0.0172274 0.0516822
k__bacteria.p__bacteroidetes 0.2156141 0.0979174 0.0236994 0.4075287 2.201999 0.0276654 0.0553308

Order (l4)

Significant only

estimate.conbf se.conbf ll.conbf ul.conbf z.conbf p.conbf p.adjust.conbf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales 0.3924806 0.0632438 0.2685252 0.5164361 6.205840 0.0000000 0.0000000
k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales 0.3158688 0.0545196 0.2090123 0.4227252 5.793673 0.0000000 0.0000001
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales -0.2179258 0.0645356 -0.3444131 -0.0914384 -3.376832 0.0007333 0.0045218
k__bacteria.p__firmicutes.c__bacilli.o__bacillales -0.1731560 0.0523488 -0.2757578 -0.0705541 -3.307732 0.0009405 0.0049715
k__bacteria.p__verrucomicrobia.c__verrucomicrobiae.o__verrucomicrobiales 0.1902859 0.0798894 0.0337055 0.3468662 2.381865 0.0172252 0.0637414
k__bacteria.p__bacteroidetes.c__bacteroidia.o__bacteroidales 0.2299773 0.1084234 0.0174714 0.4424833 2.121104 0.0339130 0.0965217
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales 0.1976105 0.0958110 0.0098243 0.3853966 2.062503 0.0391599 0.0965944

Family (l5)

Significant only

estimate.conbf se.conbf ll.conbf ul.conbf z.conbf p.conbf p.adjust.conbf
k__bacteria.p__actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae 0.3158688 0.0545196 0.2090123 0.4227252 5.793673 0.0000000 0.0000001
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae -0.2179258 0.0645356 -0.3444131 -0.0914384 -3.376832 0.0007333 0.0069659
k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae -0.1841218 0.0598847 -0.3014937 -0.0667498 -3.074603 0.0021078 0.0160195
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__peptostreptococcaceae 0.1890903 0.0716934 0.0485738 0.3296068 2.637485 0.0083523 0.0577070
k__bacteria.p__verrucomicrobia.c__verrucomicrobiae.o__verrucomicrobiales.f__verrucomicrobiaceae 0.1902859 0.0798894 0.0337055 0.3468662 2.381865 0.0172252 0.0872855
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__ruminococcaceae 0.2044635 0.0874835 0.0329990 0.3759280 2.337166 0.0194305 0.0922951
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae 0.1418926 0.0667077 0.0111478 0.2726374 2.127078 0.0334136 0.1233860
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae 0.3387166 0.1598501 0.0254162 0.6520170 2.118964 0.0340935 0.1233860
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae 0.1976105 0.0958110 0.0098243 0.3853966 2.062503 0.0391599 0.1240064

Genus (l6)

Significant only

estimate.conbf se.conbf ll.conbf ul.conbf z.conbf p.conbf p.adjust.conbf
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__coprococcus 0.3299376 0.0063390 0.3175133 0.3423619 52.048471 0.0000000 0.0000000
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia 0.3815612 0.0668433 0.2505508 0.5125716 5.708296 0.0000000 0.0000002
k__bacteria.p__firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__.eubacterium. 0.3803923 0.0787618 0.2260221 0.5347626 4.829656 0.0000014 0.0000255
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ 0.3369517 0.0781848 0.1837122 0.4901911 4.309681 0.0000163 0.0002707
k__bacteria.p__proteobacteria.c__gammaproteobacteria.o__pasteurellales.f__pasteurellaceae.g__haemophilus -0.2280437 0.0652393 -0.3559103 -0.1001770 -3.495496 0.0004732 0.0064094
k__bacteria.p__firmicutes.c__bacilli.o__bacillales.f__staphylococcaceae.g__staphylococcus -0.1829793 0.0594315 -0.2994629 -0.0664956 -3.078824 0.0020782 0.0196292
k__bacteria.p__verrucomicrobia.c__verrucomicrobiae.o__verrucomicrobiales.f__verrucomicrobiaceae.g__akkermansia 0.1907668 0.0798897 0.0341859 0.3473478 2.387877 0.0169460 0.1166765
k__bacteria.p__firmicutes.c__clostridia.o__clostridiales.f__peptostreptococcaceae.g__ 0.2910458 0.1410017 0.0146875 0.5674042 2.064129 0.0390054 0.1823383

Meta-analysis of KEGG pathway relative abundance

Change in non-exbf vs. exbf

Level 2 KEGG pathway

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Environmental.Information.Processing..Signaling.Molecules.and.Interaction -0.0481241 0.0174642 -0.0823533 -0.0138949 -2.755588 0.0058587 0.2167706
Genetic.Information.Processing..Transcription 0.0159785 0.0066440 0.0029565 0.0290005 2.404959 0.0161743 0.2992240

Nice plot

Level 3 KEGG pathway

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism 0.0748047 0.0153469 0.0447254 0.1048841 4.874259 0.0000011 0.0002436
Cellular.Processes..Transport.and.Catabolism..Peroxisome -0.0634911 0.0150010 -0.0928925 -0.0340896 -4.232451 0.0000231 0.0025774
Metabolism..Lipid.Metabolism..Fatty.acid.metabolism -0.0862183 0.0256901 -0.1365700 -0.0358667 -3.356095 0.0007905 0.0587615
Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair 0.0170264 0.0054765 0.0062925 0.0277602 3.108958 0.0018775 0.0863346
Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions 0.0618250 0.0201180 0.0223944 0.1012557 3.073116 0.0021184 0.0863346
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins 0.0758176 0.0248949 0.0270245 0.1246108 3.045504 0.0023229 0.0863346
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism -0.0286169 0.0096273 -0.0474861 -0.0097477 -2.972464 0.0029542 0.0941123
Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis 0.0415976 0.0143916 0.0133905 0.0698047 2.890399 0.0038475 0.0991170
Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes -0.0707846 0.0245939 -0.1189877 -0.0225816 -2.878143 0.0040002 0.0991170
Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway 0.0339486 0.0122911 0.0098585 0.0580387 2.762045 0.0057441 0.1280925
Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism 0.0230465 0.0101136 0.0032242 0.0428689 2.278763 0.0226811 0.4598087
Unclassified..Genetic.Information.Processing..Protein.folding.and.associated.processing -0.0110941 0.0050162 -0.0209256 -0.0012625 -2.211653 0.0269906 0.5010248
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes 0.0306056 0.0142804 0.0026166 0.0585946 2.143191 0.0320978 0.5010248
Organismal.Systems..Endocrine.System..Adipocytokine.signaling.pathway -0.1040949 0.0493909 -0.2008994 -0.0072905 -2.107573 0.0350680 0.5010248
Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms 0.0263807 0.0125598 0.0017639 0.0509975 2.100403 0.0356934 0.5010248
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 -0.0998941 0.0476250 -0.1932373 -0.0065509 -2.097515 0.0359480 0.5010248
Human.Diseases..Infectious.Diseases..Epithelial.cell.signaling.in.Helicobacter.pylori.infection 0.0476778 0.0233794 0.0018550 0.0935006 2.039309 0.0414192 0.5306485
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Nicotinate.and.nicotinamide.metabolism -0.0310355 0.0154611 -0.0613386 -0.0007324 -2.007333 0.0447142 0.5306485
Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism 0.0289152 0.0146735 0.0001557 0.0576747 1.970575 0.0487726 0.5306485

Nice plot all pathways

Nice plot significant pathways only (pooled p<0.05)

Nice plot multiple testing adjusted significant pathways only (adjusted pooled p<0.1)

Sensitivity analysis

No Haiti data

Level 2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Organismal.Systems..Environmental.Adaptation 0.0489196 0.0154245 0.0186881 0.0791511 3.171549 0.0015163 0.0561025
Environmental.Information.Processing..Signaling.Molecules.and.Interaction -0.0502036 0.0176101 -0.0847186 -0.0156885 -2.850847 0.0043603 0.0806654
Genetic.Information.Processing..Transcription 0.0163666 0.0063851 0.0038521 0.0288811 2.563255 0.0103696 0.1278916
Metabolism..Carbohydrate.Metabolism 0.0131067 0.0066302 0.0001118 0.0261016 1.976820 0.0480620 0.4445737

Level 3

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism 0.0806811 0.0162836 0.0487659 0.1125964 4.954756 0.0000007 0.0001622
Cellular.Processes..Transport.and.Catabolism..Peroxisome -0.0650783 0.0150202 -0.0945174 -0.0356393 -4.332715 0.0000147 0.0016496
Metabolism..Lipid.Metabolism..Fatty.acid.metabolism -0.0946530 0.0230876 -0.1399040 -0.0494021 -4.099727 0.0000414 0.0030885
Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions 0.0660993 0.0182315 0.0303662 0.1018323 3.625554 0.0002883 0.0161472
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins 0.0880979 0.0249367 0.0392229 0.1369729 3.532866 0.0004111 0.0184164
Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway 0.0397027 0.0119850 0.0162125 0.0631929 3.312691 0.0009240 0.0301535
Organismal.Systems..Environmental.Adaptation..Plant.pathogen.interaction 0.0459717 0.0139004 0.0187273 0.0732161 3.307211 0.0009423 0.0301535
Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis 0.0443805 0.0142821 0.0163880 0.0723729 3.107413 0.0018873 0.0528452
Organismal.Systems..Endocrine.System..Adipocytokine.signaling.pathway -0.1279232 0.0422790 -0.2107885 -0.0450579 -3.025693 0.0024806 0.0617403
Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes -0.0721010 0.0246330 -0.1203808 -0.0238213 -2.927011 0.0034224 0.0766609
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism -0.0291619 0.0100993 -0.0489563 -0.0093676 -2.887504 0.0038831 0.0790743
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Porphyrin.and.chlorophyll.metabolism 0.0626417 0.0223883 0.0187615 0.1065219 2.797970 0.0051425 0.0959931
Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms 0.0364030 0.0131564 0.0106170 0.0621889 2.766949 0.0056584 0.0974979
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes 0.0353067 0.0131936 0.0094477 0.0611657 2.676042 0.0074497 0.1191955
Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair 0.0148692 0.0057662 0.0035677 0.0261706 2.578692 0.0099175 0.1481015
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Biotin.metabolism 0.0443338 0.0180359 0.0089842 0.0796835 2.458094 0.0139677 0.1955472
Unclassified..Cellular.Processes.and.Signaling..Inorganic.ion.transport.and.metabolism -0.0854735 0.0363810 -0.1567790 -0.0141680 -2.349397 0.0188039 0.2441364
Metabolism..Lipid.Metabolism..Glycerolipid.metabolism 0.0381790 0.0163608 0.0061125 0.0702456 2.333573 0.0196181 0.2441364
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Toluene.degradation -0.0789239 0.0346447 -0.1468262 -0.0110215 -2.278094 0.0227210 0.2625265
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 -0.1077738 0.0475573 -0.2009843 -0.0145632 -2.266188 0.0234399 0.2625265
Organismal.Systems..Endocrine.System..Insulin.signaling.pathway 0.0604418 0.0275299 0.0064843 0.1143993 2.195500 0.0281278 0.2934589
Unclassified..Genetic.Information.Processing..Protein.folding.and.associated.processing -0.0110271 0.0050446 -0.0209143 -0.0011398 -2.185914 0.0288219 0.2934589
Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism 0.0214157 0.0100361 0.0017453 0.0410862 2.133866 0.0328537 0.3199669
Metabolism..Amino.Acid.Metabolism..Glycine..serine.and.threonine.metabolism -0.0151926 0.0072327 -0.0293685 -0.0010168 -2.100542 0.0356812 0.3248701
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Nicotinate.and.nicotinamide.metabolism -0.0326563 0.0156489 -0.0633275 -0.0019850 -2.086812 0.0369051 0.3248701
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Metabolism.of.xenobiotics.by.cytochrome.P450 -0.0993704 0.0480715 -0.1935887 -0.0051520 -2.067137 0.0387213 0.3248701
Genetic.Information.Processing..Translation..RNA.transport 0.0621342 0.0301254 0.0030895 0.1211788 2.062518 0.0391584 0.3248701
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Tetracycline.biosynthesis 0.0562197 0.0281702 0.0010072 0.1114323 1.995717 0.0459647 0.3514947
Genetic.Information.Processing..Folding..Sorting.and.Degradation..Proteasome -0.0963819 0.0484326 -0.1913081 -0.0014557 -1.990020 0.0465887 0.3514947
Human.Diseases..Infectious.Diseases..Epithelial.cell.signaling.in.Helicobacter.pylori.infection 0.0458477 0.0231344 0.0005050 0.0911903 1.981795 0.0475022 0.3514947
Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism 0.0282869 0.0143465 0.0001683 0.0564055 1.971696 0.0486444 0.3514947

No USA(CA_MA_MO) data

Level 2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Glycan.Biosynthesis.and.Metabolism 0.0519612 0.0115735 0.0292776 0.0746448 4.489673 0.0000071 0.0002639
Genetic.Information.Processing..Transcription 0.0140217 0.0051204 0.0039858 0.0240575 2.738388 0.0061741 0.1142213
Environmental.Information.Processing..Signaling.Molecules.and.Interaction -0.0463997 0.0186814 -0.0830146 -0.0097848 -2.483737 0.0130012 0.1603479

Level 3

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism 0.0695846 0.0140831 0.0419821 0.0971870 4.940987 0.0000008 0.0001757
Cellular.Processes..Transport.and.Catabolism..Peroxisome -0.0593738 0.0158836 -0.0905051 -0.0282425 -3.738054 0.0001854 0.0209558
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Xylene.degradation -0.0564881 0.0187429 -0.0932235 -0.0197527 -3.013838 0.0025797 0.1577341
Metabolism..Lipid.Metabolism..Fatty.acid.metabolism -0.0893386 0.0299073 -0.1479559 -0.0307213 -2.987181 0.0028156 0.1577341
Genetic.Information.Processing..Folding..Sorting.and.Degradation..Chaperones.and.folding.catalysts 0.0242035 0.0083216 0.0078934 0.0405136 2.908510 0.0036316 0.1577341
Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes -0.0711084 0.0249038 -0.1199190 -0.0222978 -2.855322 0.0042993 0.1577341
Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions 0.0503206 0.0179765 0.0150873 0.0855539 2.799240 0.0051223 0.1577341
Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis 0.0451811 0.0163033 0.0132272 0.0771349 2.771288 0.0055835 0.1577341
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins 0.0717378 0.0274480 0.0179408 0.1255348 2.613593 0.0089596 0.2249850
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism -0.0243667 0.0095096 -0.0430052 -0.0057281 -2.562315 0.0103977 0.2302045
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Biotin.metabolism 0.0480086 0.0190681 0.0106359 0.0853814 2.517749 0.0118107 0.2302045
Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway 0.0334309 0.0133423 0.0072805 0.0595814 2.505633 0.0122232 0.2302045
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes 0.0381120 0.0159839 0.0067842 0.0694399 2.384403 0.0171069 0.2973962
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Chloroalkane.and.chloroalkene.degradation -0.0440491 0.0187390 -0.0807769 -0.0073214 -2.350670 0.0187396 0.3025111
Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair 0.0136657 0.0059388 0.0020258 0.0253056 2.301070 0.0213877 0.3222407
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 -0.1047320 0.0486254 -0.2000359 -0.0094280 -2.153855 0.0312515 0.4116602
Environmental.Information.Processing..Signal.Transduction..Phosphatidylinositol.signaling.system 0.0181668 0.0084431 0.0016186 0.0347150 2.151673 0.0314231 0.4116602
Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms 0.0344968 0.0161602 0.0028235 0.0661702 2.134681 0.0327871 0.4116602
Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism 0.0224208 0.0107122 0.0014253 0.0434164 2.093014 0.0363479 0.4323489
Genetic.Information.Processing..Folding..Sorting.and.Degradation..Proteasome -0.1014237 0.0500534 -0.1995265 -0.0033209 -2.026311 0.0427329 0.4828817

No UNC data

Level 2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Environmental.Information.Processing..Signaling.Molecules.and.Interaction -0.0481241 0.0174642 -0.0823533 -0.0138949 -2.755588 0.0058587 0.2167706
Genetic.Information.Processing..Transcription 0.0159785 0.0066440 0.0029565 0.0290005 2.404959 0.0161743 0.2992240

Level 3

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism 0.0748047 0.0153469 0.0447254 0.1048841 4.874259 0.0000011 0.0002468
Cellular.Processes..Transport.and.Catabolism..Peroxisome -0.0634911 0.0150010 -0.0928925 -0.0340896 -4.232451 0.0000231 0.0026121
Metabolism..Lipid.Metabolism..Fatty.acid.metabolism -0.0862183 0.0256901 -0.1365700 -0.0358667 -3.356095 0.0007905 0.0595520
Genetic.Information.Processing..Replication.and.Repair..Base.excision.repair 0.0170264 0.0054765 0.0062925 0.0277602 3.108958 0.0018775 0.0874960
Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions 0.0618250 0.0201180 0.0223944 0.1012557 3.073116 0.0021184 0.0874960
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins 0.0758176 0.0248949 0.0270245 0.1246108 3.045504 0.0023229 0.0874960
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism -0.0286169 0.0096273 -0.0474861 -0.0097477 -2.972464 0.0029542 0.0953784
Metabolism..Lipid.Metabolism..Fatty.acid.biosynthesis 0.0415976 0.0143916 0.0133905 0.0698047 2.890399 0.0038475 0.1004504
Genetic.Information.Processing..Translation..Ribosome.biogenesis.in.eukaryotes -0.0707846 0.0245939 -0.1189877 -0.0225816 -2.878143 0.0040002 0.1004504
Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway 0.0339486 0.0122911 0.0098585 0.0580387 2.762045 0.0057441 0.1298157
Metabolism..Metabolism.of.Other.Amino.Acids..D.Alanine.metabolism 0.0230465 0.0101136 0.0032242 0.0428689 2.278763 0.0226811 0.4659945
Unclassified..Genetic.Information.Processing..Protein.folding.and.associated.processing -0.0110941 0.0050162 -0.0209256 -0.0012625 -2.211653 0.0269906 0.5077651
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes 0.0306056 0.0142804 0.0026166 0.0585946 2.143191 0.0320978 0.5077651
Organismal.Systems..Endocrine.System..Adipocytokine.signaling.pathway -0.1040949 0.0493909 -0.2008994 -0.0072905 -2.107573 0.0350680 0.5077651
Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms 0.0263807 0.0125598 0.0017639 0.0509975 2.100403 0.0356934 0.5077651
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…cytochrome.P450 -0.0998941 0.0476250 -0.1932373 -0.0065509 -2.097515 0.0359480 0.5077651
Human.Diseases..Infectious.Diseases..Epithelial.cell.signaling.in.Helicobacter.pylori.infection 0.0476778 0.0233794 0.0018550 0.0935006 2.039309 0.0414192 0.5377872
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Nicotinate.and.nicotinamide.metabolism -0.0310355 0.0154611 -0.0613386 -0.0007324 -2.007333 0.0447142 0.5377872
Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism 0.0289152 0.0146735 0.0001557 0.0576747 1.970575 0.0487726 0.5377872

stratify by birth mode

Vaginal birth

Level 2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Human.Diseases..Infectious.Diseases -0.0484981 0.0139289 -0.0757982 -0.0211980 -3.481839 0.0004980 0.0189234
Human.Diseases..Neurodegenerative.Diseases -0.0840083 0.0286090 -0.1400809 -0.0279357 -2.936430 0.0033201 0.0630826
Unclassified..Genetic.Information.Processing -0.0202899 0.0083563 -0.0366680 -0.0039118 -2.428091 0.0151785 0.1607306
Environmental.Information.Processing..Signal.Transduction -0.0553524 0.0231749 -0.1007744 -0.0099304 -2.388463 0.0169190 0.1607306
Metabolism..Metabolism.of.Other.Amino.Acids -0.0167393 0.0076171 -0.0316685 -0.0018101 -2.197602 0.0279775 0.2126291

Level 3 All

Significant only (pooled p<0.05)

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Carbohydrate.Metabolism..Pentose.phosphate.pathway 0.0461974 0.0092376 0.0280921 0.0643027 5.001030 0.0000006 0.0001306
Metabolism..Carbohydrate.Metabolism..Propanoate.metabolism -0.0570404 0.0134718 -0.0834447 -0.0306362 -4.234055 0.0000230 0.0026279
Metabolism..Lipid.Metabolism..Fatty.acid.metabolism -0.0993213 0.0284718 -0.1551250 -0.0435176 -3.488412 0.0004859 0.0370903
Metabolism..Carbohydrate.Metabolism..Fructose.and.mannose.metabolism 0.0720518 0.0219622 0.0290068 0.1150969 3.280725 0.0010354 0.0592770
Unclassified..Cellular.Processes.and.Signaling..Sporulation 0.2691131 0.0880246 0.0965881 0.4416381 3.057250 0.0022338 0.0994375
Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism 0.0398818 0.0132461 0.0139199 0.0658437 3.010830 0.0026054 0.0994375
Metabolism..Enzyme.Families..Peptidases 0.0213727 0.0072551 0.0071530 0.0355924 2.945901 0.0032202 0.1053451
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Pantothenate.and.CoA.biosynthesis 0.0229267 0.0086275 0.0060172 0.0398362 2.657414 0.0078743 0.2154792
Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms 0.0343939 0.0132238 0.0084757 0.0603121 2.600904 0.0092979 0.2154792
Metabolism..Carbohydrate.Metabolism..Butanoate.metabolism -0.0390622 0.0152890 -0.0690281 -0.0090962 -2.554916 0.0106214 0.2154792
Metabolism..Metabolism.of.Other.Amino.Acids..Glutathione.metabolism -0.0759232 0.0298932 -0.1345127 -0.0173336 -2.539817 0.0110911 0.2154792
Metabolism..Amino.Acid.Metabolism..Tryptophan.metabolism -0.1037075 0.0410760 -0.1842150 -0.0232001 -2.524773 0.0115773 0.2154792
Metabolism..Amino.Acid.Metabolism..Lysine.degradation -0.0938693 0.0377849 -0.1679263 -0.0198124 -2.484310 0.0129803 0.2154792
Environmental.Information.Processing..Membrane.Transport..Bacterial.secretion.system -0.0478536 0.0193032 -0.0856872 -0.0100200 -2.479047 0.0131734 0.2154792
Unclassified..Genetic.Information.Processing..Replication..recombination.and.repair.proteins -0.0443230 0.0181120 -0.0798220 -0.0088241 -2.447159 0.0143987 0.2198206
Environmental.Information.Processing..Signal.Transduction..Two.component.system -0.0610081 0.0262962 -0.1125477 -0.0094685 -2.320034 0.0203390 0.2911026
Metabolism..Carbohydrate.Metabolism..Pentose.and.glucuronate.interconversions 0.0597831 0.0266534 0.0075434 0.1120228 2.242983 0.0248979 0.3300089
Metabolism..Lipid.Metabolism..Sphingolipid.metabolism 0.0973295 0.0437020 0.0116751 0.1829839 2.227115 0.0259396 0.3300089
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Limonene.and.pinene.degradation -0.0813978 0.0386717 -0.1571928 -0.0056027 -2.104844 0.0353049 0.4255172
Metabolism..Amino.Acid.Metabolism..Valine..leucine.and.isoleucine.degradation -0.0895058 0.0434446 -0.1746557 -0.0043559 -2.060227 0.0393768 0.4326503
Metabolism..Energy.Metabolism..Methane.metabolism 0.0355941 0.0173608 0.0015675 0.0696207 2.050251 0.0403400 0.4326503
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Vitamin.B6.metabolism -0.0328569 0.0162180 -0.0646435 -0.0010703 -2.025958 0.0427691 0.4326503
Organismal.Systems..Endocrine.System..Insulin.signaling.pathway 0.0703689 0.0351516 0.0014731 0.1392648 2.001871 0.0452986 0.4326503
Environmental.Information.Processing..Membrane.Transport..Secretion.system -0.0508999 0.0255435 -0.1009642 -0.0008357 -1.992679 0.0462966 0.4326503
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.ansamycins 0.0805279 0.0405844 0.0009840 0.1600719 1.984209 0.0472326 0.4326503

Multiple testing adjusted Significant only (adjusted pooled p<0.1)

C-section birth

Level 2

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Human.Diseases..Infectious.Diseases -0.1126764 0.0258973 -0.1634341 -0.0619188 -4.350903 0.0000136 0.0005152
Environmental.Information.Processing..Signal.Transduction -0.1416460 0.0372818 -0.2147171 -0.0685750 -3.799334 0.0001451 0.0027566
Unclassified..Poorly.Characterized -0.1803121 0.0692044 -0.3159502 -0.0446739 -2.605500 0.0091740 0.0868309
Metabolism..Energy.Metabolism 0.0374528 0.0147283 0.0085859 0.0663197 2.542920 0.0109930 0.0868309
Human.Diseases..Neurodegenerative.Diseases -0.1436055 0.0567741 -0.2548806 -0.0323303 -2.529420 0.0114251 0.0868309
Genetic.Information.Processing..Replication.and.Repair 0.1053877 0.0472699 0.0127404 0.1980350 2.229488 0.0257815 0.1311394
Metabolism..Nucleotide.Metabolism 0.1138804 0.0510945 0.0137370 0.2140238 2.228820 0.0258259 0.1311394
Metabolism..Amino.Acid.Metabolism 0.0256341 0.0117885 0.0025291 0.0487391 2.174502 0.0296674 0.1311394
Unclassified..Cellular.Processes.and.Signaling -0.1513433 0.0711795 -0.2908525 -0.0118341 -2.126222 0.0334848 0.1311394
Metabolism..Carbohydrate.Metabolism 0.0257629 0.0121865 0.0018779 0.0496480 2.114057 0.0345104 0.1311394

Level 3 All

Significant only (pooled p<0.05)

estimate.nebf se.nebf ll.nebf ul.nebf z.nebf p.nebf p.adjust.nebf
Metabolism..Amino.Acid.Metabolism..Lysine.degradation -0.2410428 0.0589751 -0.3566318 -0.1254538 -4.087199 0.0000437 0.0052013
Environmental.Information.Processing..Membrane.Transport..Secretion.system -0.1668053 0.0409038 -0.2469752 -0.0866354 -4.077994 0.0000454 0.0052013
Cellular.Processes..Cell.Motility..Cytoskeleton.proteins 0.1946407 0.0501812 0.0962873 0.2929941 3.878754 0.0001050 0.0080144
Environmental.Information.Processing..Signal.Transduction..Two.component.system -0.1563058 0.0417345 -0.2381040 -0.0745076 -3.745237 0.0001802 0.0103178
Metabolism..Amino.Acid.Metabolism..Lysine.biosynthesis 0.0932496 0.0261242 0.0420471 0.1444521 3.569470 0.0003577 0.0147737
Metabolism..Amino.Acid.Metabolism..Tryptophan.metabolism -0.2173867 0.0612575 -0.3374491 -0.0973242 -3.548736 0.0003871 0.0147737
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Thiamine.metabolism 0.0778945 0.0227453 0.0333146 0.1224745 3.424647 0.0006156 0.0201389
Metabolism..Lipid.Metabolism..Biosynthesis.of.unsaturated.fatty.acids -0.1598449 0.0480023 -0.2539276 -0.0657621 -3.329941 0.0008686 0.0248649
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Drug.metabolism…other.enzymes 0.0699567 0.0212525 0.0283025 0.1116109 3.291685 0.0009959 0.0253399
Metabolism..Lipid.Metabolism..Fatty.acid.metabolism -0.1490154 0.0458870 -0.2389524 -0.0590784 -3.247440 0.0011645 0.0266667
Genetic.Information.Processing..Folding..Sorting.and.Degradation..Sulfur.relay.system -0.1371891 0.0433387 -0.2221315 -0.0522467 -3.165507 0.0015481 0.0299670
Metabolism..Carbohydrate.Metabolism..Galactose.metabolism 0.1151450 0.0364226 0.0437581 0.1865320 3.161364 0.0015703 0.0299670
Genetic.Information.Processing..Replication.and.Repair..Mismatch.repair 0.0832394 0.0269024 0.0305117 0.1359671 3.094126 0.0019739 0.0347716
Metabolism..Enzyme.Families..Peptidases 0.0367908 0.0120243 0.0132236 0.0603581 3.059696 0.0022156 0.0362412
Unclassified..Cellular.Processes.and.Signaling..Inorganic.ion.transport.and.metabolism -0.2280142 0.0753721 -0.3757408 -0.0802875 -3.025179 0.0024849 0.0378887
Metabolism..Biosynthesis.of.Other.Secondary.Metabolites..Phenylpropanoid.biosynthesis 0.1624628 0.0543283 0.0559814 0.2689443 2.990393 0.0027862 0.0378887
Genetic.Information.Processing..Replication.and.Repair..Nucleotide.excision.repair 0.1893190 0.0633790 0.0650986 0.3135395 2.987096 0.0028164 0.0378887
Metabolism..Carbohydrate.Metabolism..Amino.sugar.and.nucleotide.sugar.metabolism 0.0854708 0.0289330 0.0287632 0.1421784 2.954096 0.0031359 0.0378887
Genetic.Information.Processing..Replication.and.Repair..DNA.replication.proteins 0.0714555 0.0241948 0.0240344 0.1188765 2.953335 0.0031436 0.0378887
Unclassified..Cellular.Processes.and.Signaling..Sporulation 0.4258710 0.1509691 0.1299770 0.7217649 2.820915 0.0047887 0.0548305
Unclassified..Cellular.Processes.and.Signaling..Other.ion.coupled.transporters -0.1077009 0.0387677 -0.1836842 -0.0317177 -2.778113 0.0054676 0.0596224
Metabolism..Amino.Acid.Metabolism..Valine..leucine.and.isoleucine.degradation -0.1456712 0.0536771 -0.2508764 -0.0404661 -2.713844 0.0066508 0.0666673
Metabolism..Metabolism.of.Other.Amino.Acids..Glutathione.metabolism -0.1200119 0.0442873 -0.2068134 -0.0332105 -2.709852 0.0067313 0.0666673
Metabolism..Energy.Metabolism..Carbon.fixation.in.photosynthetic.organisms 0.0526806 0.0195297 0.0144032 0.0909581 2.697465 0.0069870 0.0666673
Unclassified..Metabolism..Metabolism.of.cofactors.and.vitamins -0.1333637 0.0498366 -0.2310416 -0.0356858 -2.676021 0.0074502 0.0682438
Human.Diseases..Infectious.Diseases..Vibrio.cholerae.pathogenic.cycle -0.1267418 0.0476819 -0.2201966 -0.0332870 -2.658069 0.0078590 0.0692195
Metabolism..Nucleotide.Metabolism..Pyrimidine.metabolism 0.1104510 0.0429456 0.0262793 0.1946228 2.571884 0.0101147 0.0815516
Metabolism..Amino.Acid.Metabolism..Phenylalanine..tyrosine.and.tryptophan.biosynthesis 0.0758791 0.0298059 0.0174605 0.1342977 2.545772 0.0109036 0.0815516
Unclassified..Poorly.Characterized..Function.unknown -0.1569012 0.0616904 -0.2778123 -0.0359902 -2.543364 0.0109791 0.0815516
Unclassified..Metabolism..Biosynthesis.and.biodegradation.of.secondary.metabolites -0.2158734 0.0850259 -0.3825210 -0.0492257 -2.538914 0.0111197 0.0815516
Metabolism..Enzyme.Families..Protein.kinases -0.1293255 0.0510231 -0.2293288 -0.0293222 -2.534648 0.0112560 0.0815516
Metabolism..Metabolism.of.Cofactors.and.Vitamins..One.carbon.pool.by.folate 0.1439633 0.0568953 0.0324506 0.2554761 2.530320 0.0113959 0.0815516
Metabolism..Energy.Metabolism..Methane.metabolism 0.0633891 0.0251859 0.0140256 0.1127526 2.516847 0.0118410 0.0821696
Cellular.Processes..Cell.Growth.and.Death..Cell.cycle…Caulobacter 0.2069000 0.0840125 0.0422384 0.3715616 2.462727 0.0137885 0.0928694
Genetic.Information.Processing..Replication.and.Repair..DNA.replication 0.0956593 0.0391391 0.0189480 0.1723706 2.444083 0.0145221 0.0950160
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Ubiquinone.and.other.terpenoid.quinone.biosynthesis -0.1294977 0.0541391 -0.2356083 -0.0233871 -2.391946 0.0167593 0.1066077
Human.Diseases..Infectious.Diseases..Pertussis -0.3459067 0.1463292 -0.6327068 -0.0591067 -2.363893 0.0180840 0.1119254
Metabolism..Carbohydrate.Metabolism..Starch.and.sucrose.metabolism 0.0968519 0.0416485 0.0152222 0.1784815 2.325456 0.0200476 0.1186550
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Polycyclic.aromatic.hydrocarbon.degradation 0.1728426 0.0745087 0.0268082 0.3188769 2.319764 0.0203536 0.1186550
Metabolism..Metabolism.of.Cofactors.and.Vitamins..Pantothenate.and.CoA.biosynthesis 0.0398297 0.0172203 0.0060784 0.0735810 2.312943 0.0207258 0.1186550
Unclassified..Cellular.Processes.and.Signaling..Electron.transfer.carriers -0.3152297 0.1370671 -0.5838763 -0.0465831 -2.299820 0.0214584 0.1198530
Cellular.Processes..Cell.Motility..Bacterial.motility.proteins -0.1964298 0.0868495 -0.3666517 -0.0262078 -2.261725 0.0237144 0.1292999
Metabolism..Amino.Acid.Metabolism..Amino.acid.related.enzymes 0.1029036 0.0459318 0.0128789 0.1929283 2.240355 0.0250679 0.1335010
Metabolism..Glycan.Biosynthesis.and.Metabolism..Peptidoglycan.biosynthesis 0.1655029 0.0742134 0.0200473 0.3109585 2.230095 0.0257412 0.1337390
Metabolism..Glycan.Biosynthesis.and.Metabolism..Lipopolysaccharide.biosynthesis.proteins -0.4913860 0.2211418 -0.9248161 -0.0579560 -2.222040 0.0262806 0.1337390
Unclassified..Cellular.Processes.and.Signaling..Membrane.and.intracellular.structural.molecules -0.3249676 0.1470898 -0.6132583 -0.0366768 -2.209314 0.0271528 0.1338000
Metabolism..Energy.Metabolism..Photosynthesis 0.3092523 0.1402569 0.0343539 0.5841507 2.204900 0.0274611 0.1338000
Metabolism..Energy.Metabolism..Photosynthesis.proteins 0.2924081 0.1367362 0.0244100 0.5604062 2.138483 0.0324776 0.1419698
Genetic.Information.Processing..Replication.and.Repair..Homologous.recombination 0.1521295 0.0711988 0.0125825 0.2916766 2.136688 0.0326234 0.1419698
Genetic.Information.Processing..Folding..Sorting.and.Degradation..Protein.export 0.2001823 0.0937140 0.0165063 0.3838583 2.136099 0.0326714 0.1419698
Metabolism..Lipid.Metabolism..Sphingolipid.metabolism 0.3160602 0.1483752 0.0252501 0.6068702 2.130141 0.0331600 0.1419698
Genetic.Information.Processing..Translation..Translation.factors 0.1979245 0.0932695 0.0151195 0.3807294 2.122070 0.0338319 0.1419698
Genetic.Information.Processing..Replication.and.Repair..DNA.repair.and.recombination.proteins 0.1416179 0.0668713 0.0105526 0.2726833 2.117768 0.0341947 0.1419698
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Prenyltransferases 0.0689059 0.0325922 0.0050264 0.1327855 2.114184 0.0344996 0.1419698
Unclassified..Poorly.Characterized..General.function.prediction.only -0.1479579 0.0699906 -0.2851369 -0.0107788 -2.113968 0.0345180 0.1419698
Unclassified..Cellular.Processes.and.Signaling..Signal.transduction.mechanisms -0.1179107 0.0558385 -0.2273522 -0.0084693 -2.111638 0.0347175 0.1419698
Metabolism..Metabolism.of.Other.Amino.Acids..beta.Alanine.metabolism -0.1460591 0.0714335 -0.2860661 -0.0060521 -2.044687 0.0408857 0.1642602
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Limonene.and.pinene.degradation -0.1832241 0.0900308 -0.3596813 -0.0067669 -2.035126 0.0418382 0.1651887
Metabolism..Metabolism.of.Terpenoids.and.Polyketides..Biosynthesis.of.siderophore.group.nonribosomal.peptides -0.2787206 0.1381849 -0.5495580 -0.0078831 -2.017012 0.0436943 0.1690090
Genetic.Information.Processing..Translation..Ribosome 0.2622067 0.1303595 0.0067069 0.5177065 2.011413 0.0442818 0.1690090
Metabolism..Xenobiotics.Biodegradation.and.Metabolism..Toluene.degradation -0.0942549 0.0471773 -0.1867207 -0.0017891 -1.997887 0.0457289 0.1716708
Genetic.Information.Processing..Replication.and.Repair..Chromosome 0.0281142 0.0141358 0.0004085 0.0558200 1.988861 0.0467165 0.1725498
Metabolism..Carbohydrate.Metabolism..Glyoxylate.and.dicarboxylate.metabolism -0.0952237 0.0482937 -0.1898776 -0.0005698 -1.971762 0.0486368 0.1767908

Multiple testing adjusted Significant only (adjusted pooled p<0.1)

Taxa relative abundance Bangladesh data only

All analyses are adjusted for age of infants or breastfeeding status at sample collection and accounting for repeated/longitudinal sample collection.

Mean taxa relative abundance by duration of exbf in samples > 6 months

For Subramanian (Bangladesh) data only.

Duration of exbf

Change in taxa relative abundance in duration of exclusive bf >2months vs. <=2 months

Phylum (L2)

GAMLSS

estimate se teststat pval pval.adjust ll ul
firmicutes -0.2451898 0.0633103 -3.872828 0.0001188 0.0004750 -0.3692779 -0.1211016
actinobacteria 0.2267863 0.0709545 3.196223 0.0014620 0.0029239 0.0877156 0.3658570

Order (l4)

GAMLSS

estimate se teststat pval pval.adjust ll ul
actinobacteria.c__coriobacteriia.o__coriobacteriales -0.2485089 0.0652555 -3.808244 0.0001536 0.0007304 -0.3764097 -0.1206081
firmicutes.c__bacilli.o__lactobacillales -0.2705907 0.0725448 -3.729979 0.0002087 0.0007304 -0.4127786 -0.1284028
actinobacteria.c__actinobacteria.o__bifidobacteriales 0.2543014 0.0712188 3.570704 0.0003831 0.0008938 0.1147125 0.3938904
firmicutes.c__erysipelotrichi.o__erysipelotrichales -0.1523709 0.0757899 -2.010438 0.0448092 0.0784160 -0.3009192 -0.0038227

Family (L5)

GAMLSS

estimate se teststat pval pval.adjust ll ul
firmicutes.c__bacilli.o__lactobacillales.f__lactobacillaceae -0.3106381 0.0757692 -4.099792 0.0000467 0.0006074 -0.4591458 -0.1621304
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae -0.2485089 0.0652555 -3.808244 0.0001536 0.0009984 -0.3764097 -0.1206081
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae 0.2543014 0.0712188 3.570704 0.0003831 0.0016600 0.1147125 0.3938904
bacteroidetes.c__bacteroidia.o__bacteroidales.f__prevotellaceae -0.2684295 0.0800336 -3.353959 0.0008441 0.0027434 -0.4252954 -0.1115636
firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae -0.2173273 0.0799003 -2.719980 0.0067053 0.0174337 -0.3739319 -0.0607226
firmicutes.c__bacilli.o__lactobacillales.f__enterococcaceae 0.1916596 0.0817407 2.344727 0.0193459 0.0419160 0.0314478 0.3518714
firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae -0.1523709 0.0757899 -2.010438 0.0448092 0.0789058 -0.3009192 -0.0038227
firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae -0.1427715 0.0722439 -1.976243 0.0485574 0.0789058 -0.2843695 -0.0011734

Genus (L6)

GAMLSS

estimate se teststat pval pval.adjust ll ul
firmicutes.c__bacilli.o__lactobacillales.f__lactobacillaceae.g__lactobacillus -0.3315173 0.0755929 -4.385561 0.0000135 0.0002844 -0.4796795 -0.1833552
firmicutes.c__clostridia.oclostridiales.f.g__ -0.3365252 0.0803138 -4.190128 0.0000318 0.0003341 -0.4939403 -0.1791101
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium 0.2542917 0.0712187 3.570578 0.0003833 0.0026828 0.1147032 0.3938803
firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__.ruminococcus. -0.2516784 0.0741715 -3.393195 0.0007339 0.0035457 -0.3970545 -0.1063022
bacteroidetes.c__bacteroidia.o__bacteroidales.f__prevotellaceae.g__prevotella -0.2684274 0.0800336 -3.353932 0.0008442 0.0035457 -0.4252933 -0.1115615
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__ -0.2173260 0.0720564 -3.016054 0.0026637 0.0093228 -0.3585566 -0.0760954
firmicutes.c__clostridia.o__clostridiales.f__clostridiaceae.g__ -0.2429816 0.0847773 -2.866116 0.0042923 0.0128768 -0.4091452 -0.0768181
firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__blautia -0.2191360 0.0791422 -2.768889 0.0057874 0.0151919 -0.3742548 -0.0640173
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella -0.1709286 0.0682455 -2.504614 0.0125085 0.0291864 -0.3046898 -0.0371675
firmicutes.c__erysipelotrichi.o__erysipelotrichales.f__erysipelotrichaceae.g__catenibacterium -0.2297013 0.0955240 -2.404645 0.0164716 0.0345904 -0.4169284 -0.0424743
firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ -0.1613206 0.0722253 -2.233574 0.0258594 0.0493679 -0.3028823 -0.0197590
firmicutes.c__bacilli.o__lactobacillales.f__enterococcaceae.g__enterococcus 0.1801273 0.0833351 2.161482 0.0310272 0.0542975 0.0167905 0.3434641

Modification effect of breastfeeding status on samples of patients with vs. without diarrhea at sample collection

Subramania (Bangladesh) data only.

Taxa relative abundance in samples of patients with vs. without diarrhea at sample collection

Only LME was used as GAMLSS has issues with small sample size (when stratifying). LME as showed above has lower power than GAMLSS in general.

After 6 month

Stratified by duration of exclusive bf

Family

GAMLSS

In infants with duration of exclusive bf <=2 months

estimate se teststat pval pval.adjust ll ul
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae -0.7714898 0.1993835 -3.869376 0.0001287 0.0016732 -1.1622815 -0.3806981
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae -0.6912195 0.1960537 -3.525664 0.0004749 0.0030865 -1.0754847 -0.3069543
firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae 0.5260925 0.1836007 2.865417 0.0043999 0.0190660 0.1662351 0.8859498

In infants with duration of exclusive bf >2 months

estimate se teststat pval pval.adjust ll ul ——— — ——— —– ———— — —

Genus

GAMLSS

In infants with duration of exclusive bf <=2 months

estimate se teststat pval pval.adjust ll ul
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium -0.7714809 0.1993838 -3.869325 0.0001287 0.0027034 -1.1622732 -0.3806886
firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__streptococcus 0.5243671 0.1841506 2.847491 0.0046498 0.0488224 0.1634320 0.8853023
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella -0.4992648 0.2105904 -2.370786 0.0182565 0.1237102 -0.9120220 -0.0865075
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__ -0.4975598 0.2188542 -2.273476 0.0235638 0.1237102 -0.9265141 -0.0686055

In infants with duration of exclusive bf >2 months

estimate se teststat pval pval.adjust ll ul
firmicutes.c__clostridia.o__clostridiales.f__lachnospiraceae.g__ 0.3859128 0.2285308 1.688668 0.0923469 0.9809468 -0.0620076 0.8338332

Stratified by bf status

Family

GAMLSS

In non-exclusive bf infants

estimate se teststat pval pval.adjust ll ul
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae -0.3440389 0.1461708 -2.353678 0.0189040 0.2457518 -0.6305337 -0.0575442
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae -0.2512789 0.1516653 -1.656799 0.0980727 0.4727405 -0.5485430 0.0459851

In no bf infants

estimate se teststat pval pval.adjust ll ul
firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae 2.090855 0.6246379 3.347308 0.0017567 0.0228374 0.8665651 3.3151455
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae -1.840426 0.7653211 -2.404776 0.0207821 0.1350835 -3.3404553 -0.3403967
Genus

GAMLSS

In bf infants

estimate se teststat pval pval.adjust ll ul
actinobacteria.c__coriobacteriia.o__coriobacteriales.f__coriobacteriaceae.g__collinsella -0.2910103 0.1480753 -1.965286 0.0498330 0.654706 -0.5812379 -0.0007827
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium -0.2512877 0.1516657 -1.656853 0.0980617 0.654706 -0.5485524 0.0459770

In non-bf infants

estimate se teststat pval pval.adjust ll ul
firmicutes.c__bacilli.o__lactobacillales.f__streptococcaceae.g__streptococcus 2.085766 0.6249206 3.337650 0.0018053 0.0379113 0.8609218 3.3106107
actinobacteria.c__actinobacteria.o__bifidobacteriales.f__bifidobacteriaceae.g__bifidobacterium -1.840411 0.7653209 -2.404758 0.0207830 0.2182216 -3.3404403 -0.3403824

Microbiome age in samples of infants with vs. without diarrhea at sample collection stratified by duration of exclusive breastfeeding

With GAMM fit and 95%CI.

GAM part

Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.3788398 0.2835665 36.6010753 0.0000000
dia.exbf2No.>2 months -1.1507899 0.4879123 -2.3585999 0.0186104
dia.exbf2Yes.<=2 months -1.1945958 0.4839537 -2.4684094 0.0138028
dia.exbf2Yes.>2 months -0.5097696 0.8117265 -0.6280066 0.5301988
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 5.032806 5.032806 251.05395 0
s(age.sample):dia.exbf2No.>2 months 4.191756 4.191756 149.07931 0
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 37.58735 0
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 49.79342 0

LME part

Value Std.Error DF t-value p-value
X(Intercept) 10.3788398 0.2843456 689 36.500798 0.0000000
Xdia.exbf2No.>2 months -1.1507899 0.4892527 689 -2.352138 0.0189463
Xdia.exbf2Yes.<=2 months -1.1945958 0.4852832 689 -2.461647 0.0140740
Xdia.exbf2Yes.>2 months -0.5097696 0.8139565 689 -0.626286 0.5313348
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 1.8308388 1.3486999 689 1.357484 0.1750718
Xs(age.sample):dia.exbf2No.>2 monthsFx1 1.6657329 1.3242074 689 1.257909 0.2088507
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 3.4720580 0.5678815 689 6.114054 0.0000000
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 5.0913209 0.7234958 689 7.037112 0.0000000

Test for heterogeneity (interaction)

Estimate Std. Error t value p.val
(Intercept) 3.7678265 0.3316981 11.359203 0.0000000
age.sample 0.6832422 0.0173814 39.308910 0.0000000
month.exbf2>2 months -1.2353814 0.5064509 -2.439291 0.0147161
diarrheaYes -1.0072569 0.5024243 -2.004793 0.0449851
month.exbf2>2 months:diarrheaYes 1.8143024 0.9006571 2.014421 0.0439654

Alpha diversity in samples of infants with vs. without diarrhea at sample collection stratified by duration of exclusive breastfeeding

With GAMM fit and 95%CI.

Shannon

Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0665206 0.0823202 37.2511259 0.0000000
dia.exbf2No.>2 months -0.1567257 0.1292484 -1.2125925 0.2255782
dia.exbf2Yes.<=2 months -0.5837970 0.1257959 -4.6408273 0.0000039
dia.exbf2Yes.>2 months -0.0919724 0.1901039 -0.4838004 0.6286357
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 1.000000 1.000000 511.09021 0.00e+00
s(age.sample):dia.exbf2No.>2 months 5.974811 5.974811 75.05942 0.00e+00
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 15.96302 6.93e-05
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 43.95975 0.00e+00
Value Std.Error DF t-value p-value
X(Intercept) 3.0665206 0.0824874 935 37.175641 0.0000000
Xdia.exbf2No.>2 months -0.1567257 0.1295109 935 -1.210135 0.2265328
Xdia.exbf2Yes.<=2 months -0.5837970 0.1260513 935 -4.631425 0.0000041
Xdia.exbf2Yes.>2 months -0.0919724 0.1904900 935 -0.482820 0.6293366
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 0.8428954 0.0373599 935 22.561490 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 -0.7155803 0.5005884 935 -1.429479 0.1532008
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 0.5554706 0.1393107 935 3.987278 0.0000720
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 0.9760833 0.1475164 935 6.616780 0.0000000

GAM part

Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0665206 0.0823202 37.2511259 0.0000000
dia.exbf2No.>2 months -0.1567257 0.1292484 -1.2125925 0.2255782
dia.exbf2Yes.<=2 months -0.5837970 0.1257959 -4.6408273 0.0000039
dia.exbf2Yes.>2 months -0.0919724 0.1901039 -0.4838004 0.6286357
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 1.000000 1.000000 511.09021 0.00e+00
s(age.sample):dia.exbf2No.>2 months 5.974811 5.974811 75.05942 0.00e+00
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 15.96302 6.93e-05
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 43.95975 0.00e+00

LME part

Value Std.Error DF t-value p-value
X(Intercept) 3.0665206 0.0824874 935 37.175641 0.0000000
Xdia.exbf2No.>2 months -0.1567257 0.1295109 935 -1.210135 0.2265328
Xdia.exbf2Yes.<=2 months -0.5837970 0.1260513 935 -4.631425 0.0000041
Xdia.exbf2Yes.>2 months -0.0919724 0.1904900 935 -0.482820 0.6293366
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 0.8428954 0.0373599 935 22.561490 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 -0.7155803 0.5005884 935 -1.429479 0.1532008
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 0.5554706 0.1393107 935 3.987278 0.0000720
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 0.9760833 0.1475164 935 6.616780 0.0000000

Test for heterogeneity (interaction)

Estimate Std. Error t value p.val
(Intercept) 1.8029966 0.0917394 19.653450 0.0000000
age.sample 0.1228124 0.0040692 30.181133 0.0000000
month.exbf2>2 months -0.1604039 0.1316082 -1.218798 0.2229207
diarrheaYes -0.5210706 0.1247534 -4.176806 0.0000296
month.exbf2>2 months:diarrheaYes 0.5706337 0.1973949 2.890823 0.0038423

Observed_species

Estimate Std. Error t value Pr(>|t|)
(Intercept) 151.196675 8.778134 17.2242389 0.0000000
dia.exbf2No.>2 months 6.376610 13.831570 0.4610185 0.6448877
dia.exbf2Yes.<=2 months -32.741233 8.978024 -3.6468195 0.0002795
dia.exbf2Yes.>2 months 8.288239 17.024836 0.4868322 0.6264862
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 2.241236 2.241236 186.022933 0.0000000
s(age.sample):dia.exbf2No.>2 months 4.803475 4.803475 86.288380 0.0000000
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 9.841034 0.0017564
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 34.546738 0.0000000
Value Std.Error DF t-value p-value
X(Intercept) 151.196675 8.795958 935 17.1893366 0.0000000
Xdia.exbf2No.>2 months 6.376610 13.859654 935 0.4600843 0.6455627
Xdia.exbf2Yes.<=2 months -32.741233 8.996254 935 -3.6394298 0.0002882
Xdia.exbf2Yes.>2 months 8.288239 17.059405 935 0.4858457 0.6271904
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 51.801469 8.730802 935 5.9331857 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 -19.877541 26.849174 935 -0.7403409 0.4592790
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 31.121465 9.940785 935 3.1306850 0.0017981
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 61.612841 10.503858 935 5.8657345 0.0000000

GAM part

Estimate Std. Error t value Pr(>|t|)
(Intercept) 151.196675 8.778134 17.2242389 0.0000000
dia.exbf2No.>2 months 6.376610 13.831570 0.4610185 0.6448877
dia.exbf2Yes.<=2 months -32.741233 8.978024 -3.6468195 0.0002795
dia.exbf2Yes.>2 months 8.288239 17.024836 0.4868322 0.6264862
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 2.241236 2.241236 186.022933 0.0000000
s(age.sample):dia.exbf2No.>2 months 4.803475 4.803475 86.288380 0.0000000
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 9.841034 0.0017564
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 34.546738 0.0000000

LME part

Value Std.Error DF t-value p-value
X(Intercept) 151.196675 8.795958 935 17.1893366 0.0000000
Xdia.exbf2No.>2 months 6.376610 13.859654 935 0.4600843 0.6455627
Xdia.exbf2Yes.<=2 months -32.741233 8.996254 935 -3.6394298 0.0002882
Xdia.exbf2Yes.>2 months 8.288239 17.059405 935 0.4858457 0.6271904
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 51.801469 8.730802 935 5.9331857 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 -19.877541 26.849174 935 -0.7403409 0.4592790
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 31.121465 9.940785 935 3.1306850 0.0017981
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 61.612841 10.503858 935 5.8657345 0.0000000

Test for heterogeneity (interaction)

Estimate Std. Error t value p.val
(Intercept) 67.218370 9.3632479 7.1789587 0.0000000
age.sample 8.165080 0.2895546 28.1987576 0.0000000
month.exbf2>2 months 6.548791 14.1631117 0.4623836 0.6438062
diarrheaYes -27.156839 8.8730060 -3.0606132 0.0022088
month.exbf2>2 months:diarrheaYes 28.115237 14.0092249 2.0069088 0.0447594

Pd_whole_tree

Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.1473183 0.4242502 26.2753392 0.0000000
dia.exbf2No.>2 months 0.2157323 0.6676390 0.3231271 0.7466679
dia.exbf2Yes.<=2 months -2.4295713 0.5177632 -4.6924371 0.0000031
dia.exbf2Yes.>2 months -0.1330212 0.8797770 -0.1511987 0.8798500
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 1.00000 1.00000 651.06494 0.0e+00
s(age.sample):dia.exbf2No.>2 months 4.26171 4.26171 146.66378 0.0e+00
s(age.sample):dia.exbf2Yes.<=2 months 1.00000 1.00000 20.80080 5.7e-06
s(age.sample):dia.exbf2Yes.>2 months 1.00000 1.00000 48.09244 0.0e+00
Value Std.Error DF t-value p-value
X(Intercept) 11.1473183 0.4251117 935 26.2220945 0.0000000
Xdia.exbf2No.>2 months 0.2157323 0.6689947 935 0.3224723 0.7471670
Xdia.exbf2Yes.<=2 months -2.4295713 0.5188143 935 -4.6829302 0.0000032
Xdia.exbf2Yes.>2 months -0.1330212 0.8815636 935 -0.1508923 0.8800932
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 3.9257946 0.1541688 935 25.4642659 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 0.5241955 1.3248785 935 0.3956555 0.6924494
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 2.6094784 0.5733168 935 4.5515473 0.0000060
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 4.1958246 0.6062613 935 6.9208183 0.0000000

GAM part

Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.1473183 0.4242502 26.2753392 0.0000000
dia.exbf2No.>2 months 0.2157323 0.6676390 0.3231271 0.7466679
dia.exbf2Yes.<=2 months -2.4295713 0.5177632 -4.6924371 0.0000031
dia.exbf2Yes.>2 months -0.1330212 0.8797770 -0.1511987 0.8798500
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 1.00000 1.00000 651.06494 0.0e+00
s(age.sample):dia.exbf2No.>2 months 4.26171 4.26171 146.66378 0.0e+00
s(age.sample):dia.exbf2Yes.<=2 months 1.00000 1.00000 20.80080 5.7e-06
s(age.sample):dia.exbf2Yes.>2 months 1.00000 1.00000 48.09244 0.0e+00

LME part

Value Std.Error DF t-value p-value
X(Intercept) 11.1473183 0.4251117 935 26.2220945 0.0000000
Xdia.exbf2No.>2 months 0.2157323 0.6689947 935 0.3224723 0.7471670
Xdia.exbf2Yes.<=2 months -2.4295713 0.5188143 935 -4.6829302 0.0000032
Xdia.exbf2Yes.>2 months -0.1330212 0.8815636 935 -0.1508923 0.8800932
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 3.9257946 0.1541688 935 25.4642659 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 0.5241955 1.3248785 935 0.3956555 0.6924494
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 2.6094784 0.5733168 935 4.5515473 0.0000060
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 4.1958246 0.6062613 935 6.9208183 0.0000000

Test for heterogeneity (interaction)

Estimate Std. Error t value p.val
(Intercept) 5.1010594 0.4591138 11.1106653 0.0000000
age.sample 0.5894225 0.0166254 35.4531752 0.0000000
month.exbf2>2 months 0.2033228 0.6822247 0.2980291 0.7656809
diarrheaYes -2.1261538 0.5095509 -4.1726032 0.0000301
month.exbf2>2 months:diarrheaYes 1.7538795 0.8050786 2.1785195 0.0293674

Chao1

Estimate Std. Error t value Pr(>|t|)
(Intercept) 367.19115 24.93875 14.7237215 0.0000000
dia.exbf2No.>2 months 16.36035 39.31689 0.4161151 0.6774169
dia.exbf2Yes.<=2 months -86.62794 23.43335 -3.6967801 0.0002305
dia.exbf2Yes.>2 months 28.78746 47.08230 0.6114285 0.5410577
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 1.502023 1.502023 261.413376 0.0000000
s(age.sample):dia.exbf2No.>2 months 4.677640 4.677640 76.504447 0.0000000
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 7.810505 0.0052936
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 29.909461 0.0000001
Value Std.Error DF t-value p-value
X(Intercept) 367.19115 24.98938 935 14.6938872 0.0000000
Xdia.exbf2No.>2 months 16.36035 39.39672 935 0.4152720 0.6780381
Xdia.exbf2Yes.<=2 months -86.62794 23.48093 935 -3.6892894 0.0002379
Xdia.exbf2Yes.>2 months 28.78746 47.17789 935 0.6101897 0.5418844
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 134.05173 12.55943 935 10.6733923 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 -43.96611 67.65832 935 -0.6498257 0.5159644
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 72.36395 25.94559 935 2.7890649 0.0053934
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 149.59600 27.40922 935 5.4578722 0.0000001

GAM part

Estimate Std. Error t value Pr(>|t|)
(Intercept) 367.19115 24.93875 14.7237215 0.0000000
dia.exbf2No.>2 months 16.36035 39.31689 0.4161151 0.6774169
dia.exbf2Yes.<=2 months -86.62794 23.43335 -3.6967801 0.0002305
dia.exbf2Yes.>2 months 28.78746 47.08230 0.6114285 0.5410577
edf Ref.df F p-value
s(age.sample):dia.exbf2No.<=2 months 1.502023 1.502023 261.413376 0.0000000
s(age.sample):dia.exbf2No.>2 months 4.677640 4.677640 76.504447 0.0000000
s(age.sample):dia.exbf2Yes.<=2 months 1.000000 1.000000 7.810505 0.0052936
s(age.sample):dia.exbf2Yes.>2 months 1.000000 1.000000 29.909461 0.0000001

LME part

Value Std.Error DF t-value p-value
X(Intercept) 367.19115 24.98938 935 14.6938872 0.0000000
Xdia.exbf2No.>2 months 16.36035 39.39672 935 0.4152720 0.6780381
Xdia.exbf2Yes.<=2 months -86.62794 23.48093 935 -3.6892894 0.0002379
Xdia.exbf2Yes.>2 months 28.78746 47.17789 935 0.6101897 0.5418844
Xs(age.sample):dia.exbf2No.<=2 monthsFx1 134.05173 12.55943 935 10.6733923 0.0000000
Xs(age.sample):dia.exbf2No.>2 monthsFx1 -43.96611 67.65832 935 -0.6498257 0.5159644
Xs(age.sample):dia.exbf2Yes.<=2 monthsFx1 72.36395 25.94559 935 2.7890649 0.0053934
Xs(age.sample):dia.exbf2Yes.>2 monthsFx1 149.59600 27.40922 935 5.4578722 0.0000001

Test for heterogeneity (interaction)

Estimate Std. Error t value p.val
(Intercept) 161.48642 26.4705453 6.1006078 0.0000000
age.sample 20.00874 0.7538543 26.5419213 0.0000000
month.exbf2>2 months 16.28471 40.3235504 0.4038512 0.6863222
diarrheaYes -72.27871 23.0991493 -3.1290636 0.0017536
month.exbf2>2 months:diarrheaYes 83.14805 36.4602574 2.2805118 0.0225774

Combined graph

R session information

R version 3.4.2 (2017-09-28)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gplots_3.0.1        bindrcpp_0.2        meta_4.9-0         
 [4] randomForest_4.6-12 gdata_2.18.0        scales_0.5.0       
 [7] RColorBrewer_1.1-2  geepack_1.2-1       zoo_1.8-0          
[10] itsadug_2.3         plotfunctions_1.3   mgcv_1.8-22        
[13] nlme_3.1-131        reshape2_1.4.3      lmerTest_2.0-36    
[16] sjPlot_2.4.0        sjmisc_2.6.3        lme4_1.1-15        
[19] Matrix_1.2-12       tidyr_0.7.2         dtplyr_0.0.2       
[22] data.table_1.10.4-3 dplyr_0.7.4         date_1.2-37        
[25] lubridate_1.7.1     chron_2.3-51        gmodels_2.16.2     
[28] gridExtra_2.3       plyr_1.8.4          digest_0.6.12      
[31] caret_6.0-78        ggplot2_2.2.1       lattice_0.20-35    
[34] knitr_1.17         

loaded via a namespace (and not attached):
  [1] backports_1.1.1       Hmisc_4.0-3           blme_1.0-4           
  [4] lazyeval_0.2.1        TMB_1.7.11            splines_3.4.2        
  [7] TH.data_1.0-8         foreach_1.4.3         htmltools_0.3.6      
 [10] magrittr_1.5          checkmate_1.8.5       cluster_2.0.6        
 [13] sfsmisc_1.1-1         recipes_0.1.1         modelr_0.1.1         
 [16] gower_0.1.2           dimRed_0.1.0          sandwich_2.4-0       
 [19] colorspace_1.3-2      haven_1.1.0           crayon_1.3.4         
 [22] bindr_0.1             survival_2.41-3       iterators_1.0.8      
 [25] glue_1.2.0            DRR_0.0.2             gtable_0.2.0         
 [28] ipred_0.9-6           sjstats_0.13.0        kernlab_0.9-25       
 [31] ddalpha_1.3.1         DEoptimR_1.0-8        abind_1.4-5          
 [34] mvtnorm_1.0-6         ggeffects_0.3.0       Rcpp_0.12.14         
 [37] xtable_1.8-2          merTools_0.3.0        htmlTable_1.11.0     
 [40] foreign_0.8-69        Formula_1.2-2         stats4_3.4.2         
 [43] prediction_0.2.0      lava_1.5.1            survey_3.32-1        
 [46] prodlim_1.6.1         DT_0.2                htmlwidgets_0.9      
 [49] acepack_1.4.1         modeltools_0.2-21     pkgconfig_2.0.1      
 [52] nnet_7.3-12           labeling_0.3          tidyselect_0.2.3     
 [55] rlang_0.1.4           munsell_0.4.3         tools_3.4.2          
 [58] cli_1.0.0             sjlabelled_1.0.5      broom_0.4.3          
 [61] evaluate_0.10.1       stringr_1.2.0         arm_1.9-3            
 [64] yaml_2.1.15           ModelMetrics_1.1.0    robustbase_0.92-8    
 [67] caTools_1.17.1        purrr_0.2.4           coin_1.2-2           
 [70] mime_0.5              RcppRoll_0.2.2        compiler_3.4.2       
 [73] bayesplot_1.4.0       rstudioapi_0.7.0-9000 tibble_1.3.4         
 [76] stringi_1.1.6         highr_0.6             forcats_0.2.0        
 [79] psych_1.7.8           nloptr_1.0.4          effects_4.0-0        
 [82] stringdist_0.9.4.6    pwr_1.2-1             lmtest_0.9-35        
 [85] bitops_1.0-6          httpuv_1.3.5          R6_2.2.2             
 [88] latticeExtra_0.6-28   KernSmooth_2.23-15    codetools_0.2-15     
 [91] MASS_7.3-47           gtools_3.5.0          assertthat_0.2.0     
 [94] CVST_0.2-1            rprojroot_1.2         withr_2.1.0          
 [97] mnormt_1.5-5          multcomp_1.4-8        parallel_3.4.2       
[100] grid_3.4.2            rpart_4.1-11          timeDate_3042.101    
[103] coda_0.19-1           glmmTMB_0.2.0         class_7.3-14         
[106] minqa_1.2.4           rmarkdown_1.8         snakecase_0.5.1      
[109] carData_3.0-0         shiny_1.0.5           base64enc_0.1-3